Training medical personnel using standardized patients (SPs) remains a complex challenge, requiring extensive domain expertise and role-specific practice. Most research on Large Language Model (LLM)-based simulated patients focuses on improving data retrieval accuracy or adjusting prompts through human feedback. However, this focus has overlooked the critical need for patient agents to learn a standardized presentation pattern that transforms data into human-like patient responses through unsupervised simulations. To address this gap, we propose EvoPatient, a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues, simultaneously gathering experience to improve the quality of both questions and answers, ultimately enabling human doctor training. Extensive experiments on various cases demonstrate that, by providing only overall SP requirements, our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference, while achieving an optimal balance of resource consumption after evolving over 200 cases for 10 hours, with excellent generalizability. Our system will be available at https://github.com/ZJUMAI/EvoPatient
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Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
PDF ↗Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to solve. To this end, we introduce OpenAct benchmark to evaluate the open-domain task-solving capability, which is built on human expert consultation and repositories in GitHub. It comprises 339 questions spanning 7 diverse domains that need to be solved with domain-specific methods. In our experiments, even state-of-the-art LLMs and LLM-based agents demonstrate unsatisfactory success rates, underscoring the need for a novel approach.Furthermore, we present OpenAgent, a novel LLM-based agent system that can tackle evolving queries in open domains through autonomously integrating specialized tools from GitHub. OpenAgent employs 1) a hierarchical framework where specialized agents handle specific tasks and can assign tasks to inferior agents, 2) a bi-level experience learning mechanism to learn from both humans’ and its own experiences to tackle tool flaws. Experiments demonstrate its superior effectiveness and efficiency, which significantly outperforms baselines. Our data and code are open-source at https://github.com/OpenBMB/OpenAct.
Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments
PDF ↗Information extraction (IE) from Visually Rich Documents (VRDs) containing layout features along with text is a critical and well-studied task. Specialized non-LLM NLP-based solutions typically involve training models using both textual and geometric information to label sequences/tokens as named entities or answers to specific questions. However, these approaches lack reasoning, are not able to infer values not explicitly present in documents, and do not generalize well to new formats. Generative LLMs-based approaches proposed recently are capable of reasoning, but struggle to comprehend clues from document layout especially in previously unseen document formats, and do not show competitive performance in heterogeneous VRD benchmark datasets. In this paper, we propose BLOCKIE, a novel LLM-based approach that organizes VRDs into localized, reusable semantic textual segments called \textit{semantic blocks}, which are processed independently. Through focused and more generalizable reasoning,our approach outperforms the state-of-the-art on public VRD benchmarks by 1-3% in F1 scores, is resilient to document formats previously not encountered and shows abilities to correctly extract information not explicitly present in documents.
Chain-of-Though (CoT) represents a common strategy for reasoning in Large Language Models (LLMs) by decomposing complex tasks into intermediate inference steps. However, explanations generated via CoT are susceptible to content biases that negatively affect their robustness and faithfulness. To mitigate existing limitations, recent work has proposed using logical formalisms coupled with external symbolic solvers. However, fully symbolic approaches possess the bottleneck of requiring a complete translation from natural language to formal languages, a process that affects efficiency and flexibility. To achieve a trade-off, this paper investigates methods to disentangle content from logical reasoning without a complete formalisation. In particular, we present QuaSAR (for Quasi-Symbolic Abstract Reasoning), a variation of CoT that guides LLMs to operate at a higher level of abstraction via quasi-symbolic explanations. Our framework leverages the capability of LLMs to formalise only relevant variables and predicates, enabling the coexistence of symbolic elements with natural language. We show the impact of QuaSAR for in-context learning and for constructing demonstrations to improve the reasoning capabilities of smaller models. Our experiments show that quasi-symbolic abstractions can improve CoT-based methods by up to 8% accuracy, enhancing robustness and consistency on challenging adversarial variations on both natural language (i.e. MMLU-Redux) and symbolic reasoning tasks (i.e., GSM-Symbolic).
ImpliHateVid: A Benchmark Dataset and Two-stage Contrastive Learning Framework for Implicit Hate Speech Detection in Videos
PDF ↗The existing research has primarily focused on text and image-based hate speech detection, video-based approaches remain underexplored. In this work, we introduce a novel dataset, ImpliHateVid, specifically curated for implicit hate speech detection in videos. ImpliHateVid consists of 2,009 videos comprising 509 implicit hate videos, 500 explicit hate videos, and 1,000 non-hate videos, making it one of the first large-scale video datasets dedicated to implicit hate detection. We also propose a novel two-stage contrastive learning framework for hate speech detection in videos. In the first stage, we train modality-specific encoders for audio, text, and image using contrastive loss by concatenating features from the three encoders. In the second stage, we train cross-encoders using contrastive learning to refine multimodal representations. Additionally, we incorporate sentiment, emotion, and caption-based features to enhance implicit hate detection. We evaluate our method on two datasets, ImpliHateVid for implicit hate speech detection and another dataset for general hate speech detection in videos, HateMM dataset, demonstrating the effectiveness of the proposed multimodal contrastive learning for hateful content detection in videos and the significance of our dataset.
The knowledge within large language models (LLMs) may become outdated quickly. While in-context editing (ICE) is currently the most effective method for knowledge editing (KE), it is constrained by the black-box modeling of LLMs and thus lacks interpretability. Our work aims to elucidate the superior performance of ICE in KE by analyzing the impacts of in-context new knowledge on token-wise distributions. We observe that despite a significant boost in logits of the new knowledge, the performance of ICE is still hindered by stubborn knowledge. We propose a novel approach termed Decoding by Contrasting Knowledge (DeCK). DeCK derives the distribution of the next token by contrasting the logits obtained from the newly edited knowledge guided by ICE with those from the unedited parametric knowledge. Our experiments demonstrate that DeCK enhances the confidence of LLMs in edited facts. For instance, it improves the performance of LLaMA3-8B-instruct on MQuAKE by up to 219%, demonstrating its capability to strengthen ICE. DeCK can be easily integrated into any ICE method as a decoding component to enhance editing capabilities.
Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., “I don’t know”) overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs’ ability of recognizing and addressing the source of uncertainty, we introduce ConfuseBench, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capability, and query ambiguity. Experiments with ConfuseBench reveal that current LLMs struggle to accurately identify the root cause of uncertainty and solve it. They prefer to attribute uncertainty to query ambiguity while overlooking capability limitations, especially for those weaker models. To tackle this challenge, we first generate context-aware inquiries that highlight the confusing aspect of the original query. Then we judge the source of uncertainty based on the uniqueness of the inquiry’s answer. Further we use an on-policy training method, InteractDPO to generate better inquiries. Experimental results demonstrate the efficacy of our approach.
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation
PDF ↗Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for this capability. To fill this gap, we introduce FEA-Bench, a benchmark designed to assess the ability of large language models (LLMs) to perform incremental development within code repositories. We collect pull requests from 83 GitHub repositories and use rule-based and intent-based filtering to construct task instances focused on new feature development. Each task instance containing code changes is paired with relevant unit test files to ensure that the solution can be verified. The feature implementation requires LLMs to simultaneously possess code completion capabilities for new components and code editing abilities for other relevant parts in the code repository, providing a more comprehensive evaluation method of LLMs’ automated software engineering capabilities.Experimental results show that LLMs perform significantly worse in the FEA-Bench, highlighting considerable challenges in such repository-level incremental code development.
Value Portrait: Assessing Language Models’ Values through Psychometrically and Ecologically Valid Items
PDF ↗The importance of benchmarks for assessing the values of language models has been pronounced due to the growing need of more authentic, human-aligned responses. However, existing benchmarks rely on human or machine annotations that are vulnerable to value-related biases. Furthermore, the tested scenarios often diverge from real-world contexts in which models are commonly used to generate text and express values. To address these issues, we propose the Value Portrait benchmark, a reliable framework for evaluating LLMs’ value orientations with two key characteristics. First, the benchmark consists of items that capture real-life user-LLM interactions, enhancing the relevance of assessment results to real-world LLM usage. Second, each item is rated by human subjects based on its similarity to their own thoughts, and correlations between these ratings and the subjects’ actual value scores are derived. This psychometrically validated approach ensures that items strongly correlated with specific values serve as reliable items for assessing those values. Through evaluating 44 LLMs with our benchmark, we find that these models prioritize Benevolence, Security, and Self-Direction values while placing less emphasis on Tradition, Power, and Achievement values. Also, our analysis reveals biases in how LLMs perceive various demographic groups, deviating from real human data.
Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution categories, relying on manual annotations, and failing to compare attributions with only subtle differences. To bridge these gaps, we introduce Complex Attributed Question Answering (CAQA), a large-scale benchmark containing comprehensive attribution categories, automatically generated using Knowledge Graphs (KGs), and complex attribution scenarios. We have conducted extensive experiments to verify the effectiveness of CAQA, including the benchmarking of 25 automatic evaluators, their comparison with human evaluators, the testing of LLM evaluators fine-tuned by CAQA and so on. These experiments also lead to a series of important findings that can benefit the future research of AQA.
Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. Unlike standard video captioning, it involves not only describing key visual details but also inferring plots that unfold across multiple movie shots, presenting distinct and complex challenges. To advance this field, we introduce Movie101v2, a large-scale, bilingual dataset with enhanced data quality specifically designed for movie narration. Revisiting the task, we propose breaking down the ultimate goal of automatic movie narration into three progressive stages, offering a clear roadmap with corresponding evaluation metrics. Based on our new benchmark, we baseline a range of large vision-language models and conduct an in-depth analysis of the challenges in movie narration generation. Our findings highlight that achieving applicable movie narration generation is a fascinating goal that requires significant research.
DualGuard: A Parameter Space Transformation Approach for Bidirectional Defense in Split-Based LLM Fine-Tuning
PDF ↗Integrating split learning with large language model fine-tuning (LLM-FT) enables secure collaboration between a trusted local client and a well-equipped remote server, but it is vulnerable to data reconstruction attacks (DRAs) that exploit transmitted activations and gradients. Current defense methods, like adding noise to activations or gradients, often sacrifice task-specific model performance under strict privacy constraints. This paper introduces DualGuard, a bidirectional defense mechanism against DRAs for split-based LLM-FT. DualGuard proposes a local warm-up parameter space transformation to alter client-side model parameters before training, using multi-task learning to strike a balance between privacy protection and model performance. Additionally, a global fine-tuning parameter space retention strategy prevents the model from reverting to vulnerable states during formal fine-tuning. Experiments show that DualGuard outperforms current defense methods against various DRAs, while maintaining task performance. Our code will be made publicly available.
Recent advancements in probing Large Language Models (LLMs) have explored their latent potential as personalized travel planning agents, though this remains a rather nascent field. Existing benchmarks, such as TravelPlanner and TravelPlanner+, rely on semi-synthetic data as well ignoring several key components of travel planning, limiting their real-world applicability. Therefore, we introduce TripCraft, a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization. Our dataset enables more detailed trip itinerary generation (including duration spent at each point of interest based on users’ persona, transit between two points of interest, etc.) while ensuring spatio-temporal consistency. Further, we propose novel evaluation metrics (temporal meal score, attraction score, spatial score, ordering score, and persona score) to assess LLM-generated plans across temporal, spatial, sequential, and personal dimensions, overcoming the limitations of commonsense and hard constraint metrics. Interestingly, our parameter-informed setting significantly enhances meal scheduling, improving performance from 61% to 80% in the 7-day scenario- as quantified by a 19% gain in our temporal meal score. Moreover, TripCraft serves as a high-quality benchmark for advancing personalized LLM-driven travel planning.
Large language models (LLMs) have demonstrated significant advancements in error handling. Current error-handling works are performed in a passive manner, with explicit error-handling instructions. However, in real-world scenarios, explicit error-handling instructions are usually unavailable. In this paper, our work identifies this challenge as how to conduct proactive error handling without explicit error handling instructions. To promote further research, this work introduces a new benchmark, termed Mis-prompt, consisting of four evaluation tasks, an error category taxonomy, and a new evaluation dataset. Furthermore, this work analyzes current LLMs’ performance on the benchmark, and the experimental results reveal that current LLMs show poor performance on proactive error handling, and SFT on error handling instances improves LLMs’ proactive error handling capabilities. The dataset will be publicly available.
Can’t See the Forest for the Trees: Benchmarking Multimodal Safety Awareness for Multimodal LLMs
PDF ↗Multimodal Large Language Models (MLLMs) have expanded the capabilities of traditional language models by enabling interaction through both text and images. However, ensuring the safety of these models remains a significant challenge, particularly in accurately identifying whether multimodal content is safe or unsafe—a capability we term safety awareness. In this paper, we introduce MMSafeAware, the first comprehensive multimodal safety awareness benchmark designed to evaluate MLLMs across 29 safety scenarios with 1,500 carefully curated image-prompt pairs. MMSafeAware includes both unsafe and over-safety subsets to assess models’ abilities to correctly identify unsafe content and avoid over-sensitivity that can hinder helpfulness. Evaluating nine widely used MLLMs using MMSafeAware reveals that current models are not sufficiently safe and often overly sensitive; for example, GPT-4V misclassifies 36.1% of unsafe inputs as safe and 59.9% of benign inputs as unsafe. We further explore three methods to improve safety awareness—prompting-based approaches, visual contrastive decoding, and vision-centric reasoning fine-tuning—but find that none achieve satisfactory performance. Our findings highlight the profound challenges in developing MLLMs with robust safety awareness, underscoring the need for further research in this area. All the code and data will be publicly available to facilitate future research.
Analyzing the Rapid Generalization of SFT via the Perspective of Attention Head Activation Patterns
PDF ↗LLMs’ performance on complex tasks is still unsatisfactory. A key issue is that presently LLMs learn in a data-driven schema, while the instructions about these complex tasks are both scarce and hard to collect or construct. On the contrary, a prominent phenomenon is that LLMs can learn rather fast on simpler tasks with adequate prior knowledge captured during pretraining stage. Thus, if the prerequisite and mechanism of such rapid generalization could be elucidated, it could enhance the efficiency and effectiveness of the LLM’s ability to learn complex tasks. Thus, in this paper, we employ a gradient-based method, to dissect the process that the SFT process adapts LLMs to downstream tasks via the perspective of attention patterns. We find that: (1) LLMs selectively activate task-specific attention heads during SFT; (2) activation patterns for complex tasks are combinations of basic task patterns; and (3) changes in a few parameters can significantly impact activation patterns after SFT on a small number of samples.Based on these insights, experiments are conducted to actually enhance the efficiency and effectiveness of SFT.
Retrieval-augmented generation (RAG) systems often struggle with narrative-rich documents and event-centric reasoning, particularly when synthesizing information across multiple sources. We present EventRAG, a novel framework that enhances text generation through structured event representations. We first construct an Event Knowledge Graph by extracting events and merging semantically equivalent nodes across documents, while expanding under-connected relationships. We then employ an iterative retrieval and inference strategy that explicitly captures temporal dependencies and logical relationships across events. Experiments on UltraDomain and MultiHopRAG benchmarks show EventRAG’s superiority over baseline RAG systems, with substantial gains in generation effectiveness, logical consistency, and multi-hop reasoning accuracy. Our work advances RAG systems by integrating structured event semantics with iterative inference, particularly benefiting scenarios requiring temporal and logical reasoning across documents.
As a bridge between natural texts and information systems like structured storage, statistical analysis, retrieving, and recommendation, the text-to-table task has received widespread attention recently. Existing researches have gone through a paradigm shift from traditional bottom-up IE (Information Extraction) to top-down LLMs-based question answering with RAG (Retrieval-Augmented Generation). Furthermore, these methods mainly adopt end-to-end models or use multi-stage pipelines to extract text content based on static table structures. However, they neglect to deal with precise inner-document evidence extraction and dynamic information such as multiple entities and events, which can not be defined in static table head format and are very common in natural texts.To address this issue, we propose a two-stage dynamic content extraction agent framework called TST (Text-Schema-Table), which uses type recognition methods to extract context evidences with the conduction of domain schema sequentially. Based on the evidence, firstly we quantify the total instances of each dynamic object and then extract them with ordered numerical prompts. Through extensive comparisons with existing methods across different datasets, our extraction framework exhibits state-of-the-art (SOTA) performance. Our codes are available at https://github.com/jiangpw41/TST.
Question Answering (QA) accounts for a significant portion of LLM usage in the wild”. However, LLMs sometimes produce false or misleading responses, also known as hallucinations”. Therefore, grounding the generated answers in contextually provided information—i.e., providing evidence for the generated text—is paramount for LLMs’ trustworthiness. Providing this information is the task of context attribution. In this paper, we systematically study LLM-based approaches for this task, namely we investigate (i) zero-shot inference, (ii) LLM ensembling, and (iii) fine-tuning of small LMs on synthetic data generated by larger LLMs. Our key contribution is SynQA: a novel generative strategy for synthesizing context attribution data. Given selected context sentences, an LLM generates QA pairs that are supported by these sentences. This leverages LLMs’ natural strengths in text generation while ensuring clear attribution paths in the synthetic training data. We show that the attribution data synthesized via SynQA is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. Finally, with a user study, we validate the usefulness of small LMs (fine-tuned on synthetic data from SynQA) in context attribution for QA.
An extensive high-quality instruction dataset is crucial for the instruction tuning process of Large Language Models (LLMs). Recent instruction expansion methods have demonstrated their capability to improve the quality and quantity of existing datasets, by prompting high-performance LLM to generate multiple new instructions from the original ones. However, existing methods focus on constructing multi-perspective prompts (e.g., increasing complexity or difficulty) to expand instructions, overlooking the “Fixed Thinking Pattern” issue of LLMs. This issue arises when repeatedly using the same set of prompts, causing LLMs to rely on a limited set of certain expressions to expand all instructions, potentially compromising the diversity of the final expanded dataset. This paper theoretically analyzes the causes of the “Fixed Thinking Pattern”, and corroborates this phenomenon through multi-faceted empirical research. Furthermore, we propose a novel method based on dynamic prompt updating: Global Eye. Specifically, after a fixed number of instruction expansions, we analyze the statistical characteristics of newly generated instructions and then update the prompts. Experimental results show that our method enables Llama3-8B and Llama2-13B to surpass the performance of open-source LLMs and GPT3.5 across various metrics. Our code and data are submitted to the Software & Data option.