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In this paper, we present an innovative process-oriented math process reward model called Math-shepherd, which assigns a reward score to each step of math problem solutions. The training of Math-shepherd is achieved using automatically constructed process-wise supervision data, breaking the bottleneck of heavy reliance on manual annotation in existing work. We explore the effectiveness of Math-shepherd in two scenarios: 1) $\textit{Verification}$: Math-shepherd is utilized for reranking multiple outputs generated by Large Language Models (LLMs); 2) $\textit{Reinforcement Learning (RL)}$: Math-shepherd is employed to reinforce LLMs.With Math-shepherd, a series of open-source LLMs demonstrates exceptional performance. For instance, process RL with Math-shepherd significantly enhances Mistral-7B (77.9\%$\to$84.1\% on GSM8K and 28.6\%$\to$33.0\% on MATH).The accuracy can be further improved to 89.1\% and 43.5\% on two benchmarks with verification of Math-shepherd.We believe that automatic process supervision holds significant potential for the future evolution of LLMs.

ACL 2024

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

process reward models

mathematical reasoning

large language models

poster

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Video storytelling is engaging multimedia content that utilizes video and its accompanying narration to share a story and attract the audience, where a key challenge is creating narrations for recorded visual scenes. Previous studies on dense video captioning and video story generation have made some progress. However, in practical applications, we typically require synchronized narrations for ongoing visual scenes. In this work, we introduce a new task of Synchronized Video Storytelling, which aims to generate synchronous and informative narrations for videos. These narrations, associated with each video clip, should relate to the visual content, integrate relevant knowledge, and have an appropriate word count corresponding to the clip's duration. Specifically, a structured storyline is beneficial to guide the generation process, ensuring coherence and integrity. To support the exploration of this task, we introduce a new benchmark dataset E-SyncVidStory with rich annotations. Since existing Multimodal LLMs are not effective in addressing this task in one-shot or few-shot settings, we propose a framework named VideoNarrator that can generate a storyline for input videos and simultaneously generate narrations with the guidance of the generated or predefined storyline. We further introduce a set of evaluation metrics to thoroughly assess the generation. Both automatic and human evaluations validate the effectiveness of our approach. Our dataset, codes, and evaluations will be released.

Synchronized Video Storytelling: Generating Video Narrations with Structured Storyline

Security practitioners maintain vulnerability reports (e.g., GitHub Advisory) to help developers mitigate security risks. An important task for these databases is automatically extracting structured information mentioned in the report, e.g., the affected software packages, to accelerate the defense of the vulnerability ecosystem.

However, it is challenging for existing work on affected package identification to achieve high precision. One reason is that all existing work focuses on relatively smaller models, thus they cannot harness the knowledge and semantic capabilities of large language models.

To address this limitation, we propose VulLibGen, the first method to use LLM for affected package identification. In contrast to existing work, VulLibGen proposes the novel idea to directly generate the affected package. To improve the precision, VulLibGen employs supervised fine-tuning (SFT), retrieval augmented generation (RAG) and a local search algorithm. The local search algorithm is a novel post-processing algorithm we introduce for reducing the hallucination of the generated packages. Our evaluation results show that VulLibGen has an average precision of 0.806 for identifying vulnerable packages in the four most popular ecosystems in GitHub Advisory (Java, JS, Python, Go) while the best average precision in previous work is 0.721. Additionally, VulLibGen has high value to security practice: we submitted 60 <vulnerability, affected package> pairs to GitHub Advisory (covers four ecosystems) and 34 of them have been accepted and merged.

VulLibGen: Generating Names of Vulnerability-Affected Packages via a Large Language Model

Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks. To enhance the capabilities of LLMs to emulate human reasoning, prior studies have focused on modeling reasoning steps using various thought structures like chains, trees, or graphs. However, LLM-based reasoning still encounters the following challenges: (1) Limited adaptability of preset structures to diverse tasks; (2) Insufficient precision in exploiting known conditions to derive new ones; and (3) Inadequate consideration of historical reasoning experiences for subsequent reasoning steps. To this end, we propose DetermLR, a novel perspective that rethinks the reasoning process as an evolution from indeterminacy to determinacy. First, we categorize known conditions into two types: determinate and indeterminate premises, facilitating the transformation process. Subsequently, we leverage quantitative measurements to prioritize more relevant premises to explore new insights. Furthermore, we automate the storage and extraction of available premises and reasoning paths with reasoning memory, preserving historical reasoning details for subsequent reasoning steps. Comprehensive experimental results demonstrate that DetermLR surpasses all baselines on various logical reasoning benchmarks: LogiQA, ProofWriter, FOLIO, PrOntoQA, and LogicalDeduction. Compared to previous multi-step reasoning methods, DetermLR achieves higher accuracy with fewer reasoning steps, highlighting its superior efficiency and effectiveness in solving logical reasoning tasks.

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy

Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integrating knowledge from external databases to mitigate these challenges. 
However, inappropriate retrieved passages can potentially hinder the LLMs' capacity to generate comprehensive and high-quality responses. 
Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability. 
In this study, we initially investigate retrieval noises and categorize them into three distinct types, reflecting real-world environments. We analyze the impact of these various retrieval noises on the robustness of LLMs. Subsequently, we propose a novel RAG approach known as Retrieval-augmented Adaptive Adversarial Training (RAAT). RAAT leverages adaptive adversarial training to dynamically adjust the model's training process in response to retrieval noises. Concurrently, it employs multi-task learning to ensure the model's capacity to internally recognize noisy contexts. Extensive experiments demonstrate that the LLaMA-2 7B model trained using RAAT exhibits significant improvements in F1 and EM scores under diverse noise conditions. For reproducibility, we will release our code and data upon acceptance.

Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training

Recent studies have shown that integrating constructional information can improve the performance of pre-trained language models (PLMs) in natural language understanding. However, exploration into leveraging constructional information to enhance generative language models for natural language generation has been limited. Additionally, probing studies indicate that PLMs primarily grasp the syntactic structure of constructions but struggle to capture their semantics. In this work, we encode constructions as inductive biases to explicitly embed constructional semantics and guide the generation process. We begin by presenting a construction grammar induction framework designed to automatically identify constructions from corpora. Subsequently, we propose the Construction-Enhanced Language Model (CoELM). It introduces a construction-guided language modeling approach that employs a dynamic sequence reassembly strategy during pre-training. Extensive experiments have demonstrated the superiority of CoELM across various benchmarks.

CoELM: Construction-Enhanced Language Modeling

Automated teaching assistants and chatbots have significant potential to reduce the workload of human instructors, especially for logistics-related question answering, which is important to students yet repetitive for instructors. However, due to privacy concerns, there is a lack of publicly available datasets. We introduce SyllabusQA, an open-source dataset with 63 real course syllabi covering 36 majors, containing 5,078 open-ended course logistics-related question-answer pairs that are diverse in both question types and answer formats. Since many logistics-related questions contain critical information like the date of an exam, it is important to evaluate the factuality of answers. We benchmark several strong baselines on this task, from large language model prompting to retrieval-augmented generation. We introduce Fact-QA, an LLM-based (GPT-4) evaluation metric to evaluate the factuality of predicted answers. We find that despite performing close to humans on traditional metrics of textual similarity, there remains a significant gap between automated approaches and humans in terms of fact precision.

SyllabusQA: A Course Logistics Question Answering Dataset

Recently, Temporal Knowledge Graph Forecasting (TKGF) has emerged as a pivotal domain for forecasting future events. Unlike black-box neural network methods, rule-based approaches are lauded for their efficiency and interpretability. For this line of work, it is crucial to correctly estimate the predictive effectiveness of the rules, i.e., the confidence. However, the existing literature lacks in-depth investigation into how confidence evolves with time. Moreover, inaccurate and heuristic confidence estimation limits the performance of rule-based methods. To alleviate such issues, we propose a framework named \textbf{TempValid} to explicitly model the temporal validity of rules for TKGF. Specifically, we design a time function to model the interaction between temporal information with confidence. TempValid conceptualizes confidence and other coefficients as learnable parameters to avoid inaccurate estimation and combinatorial explosion. Furthermore, we introduce a \textit{rule-adversarial negative sampling} and a \textit{time-aware negative sampling} strategies to facilitate TempValid learning. Extensive experiments show that TempValid significantly outperforms previous state-of-the-art (SOTA) rule-based methods on six TKGF datasets. Moreover, it exhibits substantial advancements in cross-domain and resource-constrained rule learning scenarios.

Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting

It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don't fit the online custom service scenarios well due to the lack of attention to user questions and possible responses. Hence, we propose to develop a time-saving and careful reading assistant for CSRs, named CARE. It can help the CSRs quickly find proper responses from the user manuals via explicit clue chains. Specifically, each of the clue chains is formed by inferring over the user manuals, starting from the question clue aligned with the user question and ending at a possible response. To overcome the shortage of supervised data, we adopt the self-supervised strategy for model learning. The offline experiment shows that CARE is efficient in automatically inferring accurate responses from the user manual. The online experiment further demonstrates the superiority of CARE to reduce CSRs' reading burden and keep high service quality, in particular with >35% decrease in time spent and keeping a >0.75 ICC score.

CARE: A Clue-guided Assistant for CSRs to Read User Manuals

Large Language Models (LLMs) demonstrate significant value in domain-specific applications, benefiting from their fundamental capabilities. Nevertheless, it is still unclear which fundamental capabilities contribute to success in specific domains. Moreover, the existing benchmark-based evaluation cannot effectively reflect the performance of real-world applications. In this survey, we review recent advances of LLMs in domain applications, aiming to summarize the fundamental capabilities and their collaboration. Furthermore, we establish connections between fundamental capabilities and specific domains, evaluating the varying importance of different capabilities. Based on our findings, we propose a reliable strategy for domains to choose more robust backbone LLMs for real-world applications.

Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey

In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models (LLMs) significantly affects their performance in tasks demanding a high degree of context awareness, such as utilizing LLMs for tool-use. Specifically, the crucial information in the context will be potentially overlooked by model when it is positioned in the trough zone of the attention waveform, leading to decreased performance. To address this issue, we propose a novel inference method named Attention Buckets. It allows LLMs to process their input through multiple parallel processes. Each process utilizes a distinct base angle for the rotary position embedding, thereby creating a unique attention waveform. By compensating an attention trough of a particular process with an attention peak of another process, our approach enhances LLM's awareness to various contextual positions, thus mitigating the risk of overlooking crucial information. In the largest tool-use benchmark, our method elevates a 7B model to achieve state-of-the-art performance, comparable to that of GPT-4. On other benchmarks and some RAG tasks, which also demand a thorough understanding of contextual content, Attention Buckets also exhibited notable enhancements in performance.

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Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

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Synchronized Video Storytelling: Generating Video Narrations with Structured Storyline

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