Papers StrategyQA
“StrategyQA” 태그가 달린 논문 40편 · 필터 해제
Fusing Bidirectional Chains of Thought and Reward Mechanisms A Method for Enhancing Question-Answering Capabilities of Large Language Models for Chinese Intangible Cultural Heritage
The rapid development of large language models (LLMs) has provided significant support and opportunities for the advancement of domain-specific LLMs. However, fine-tuning these large models using Intangible Cultural Heri…
Knowledge DistillationLarge Language ModelQuestion AnsweringStrategyQARule-Guided Feedback: Enhancing Reasoning by Enforcing Rule Adherence in Large Language Models
In this paper, we introduce Rule-Guided Feedback (RGF), a framework designed to enhance Large Language Model (LLM) performance through structured rule adherence and strategic information seeking. RGF implements a teacher…
Checkmate In OneGSM8KLanguage ModelingLanguage Modelling+3DeLTa: A Decoding Strategy based on Logit Trajectory Prediction Improves Factuality and Reasoning Ability
Large Language Models (LLMs) are increasingly being used in real-world applications. However, concerns about the reliability of the content they generate persist, as it frequently deviates from factual correctness or exh…
GSM8KLogical ReasoningStrategyQATrajectory Prediction+1Voting or Consensus? Decision-Making in Multi-Agent Debate
Much of the success of multi-agent debates depends on carefully choosing the right parameters. The decision-making protocol stands out as it can highly impact final model answers, depending on how decisions are reached. …
Decision MakingMMLUStrategyQAUnraveling Indirect In-Context Learning Using Influence Functions
This work introduces a novel paradigm for generalized In-Context Learning (ICL), termed Indirect In-Context Learning. In Indirect ICL, we explore demonstration selection strategies tailored for two distinct real-world sc…
In-Context LearningInformativenessMMLUStrategyQAAutoReason: Automatic Few-Shot Reasoning Decomposition
Chain of Thought (CoT) was introduced in recent research as a method for improving step-by-step reasoning in Large Language Models. However, CoT has limited applications such as its need for hand-crafted few-shot exempla…
StrategyQADialectical Behavior Therapy Approach to LLM Prompting
Large language models demonstrated state-of-the-art results on various reasoning tasks when applying the chain-of-thought (CoT) prompting technique. CoT prompting guides the model into breaking tasks into a few intermedi…
GSM8KStrategyQARationale-Aware Answer Verification by Pairwise Self-Evaluation
Answer verification identifies correct solutions among candidates generated by large language models (LLMs). Current approaches typically train verifier models by labeling solutions as correct or incorrect based solely o…
ARCStrategyQAvalidA Looming Replication Crisis in Evaluating Behavior in Language Models? Evidence and Solutions
In an era where large language models (LLMs) are increasingly integrated into a wide range of everyday applications, research into these models' behavior has surged. However, due to the novelty of the field, clear method…
Prompt EngineeringStrategyQAProof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning
Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel domains and complex logical sequences. This research introduces Proof of …
BenchmarkingFormal LogicMultimodal ReasoningProgram Synthesis+1Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
This paper introduces rStar, a self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into…
GSM8KMathStrategyQAMeta-prompting Optimized Retrieval-augmented Generation
Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible…
Multi-hop Question AnsweringQuestion AnsweringRetrievalRetrieval-augmented Generation+1Question-Analysis Prompting Improves LLM Performance in Reasoning Tasks
Although LLMs have the potential to transform many fields, they still underperform humans in reasoning tasks. Existing methods induce the model to produce step-by-step calculations, but this research explores the questio…
GSM8KStrategyQAAdvancing Process Verification for Large Language Models via Tree-Based Preference Learning
Large Language Models (LLMs) have demonstrated remarkable potential in handling complex reasoning tasks by generating step-by-step rationales.Some methods have proven effective in boosting accuracy by introducing extra v…
Binary ClassificationGSM8KMathStrategyQAUnchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activates a different subset of experts determi…
Computational EfficiencyGSM8KHumanEvalmbpp+2Improving Attributed Text Generation of Large Language Models via Preference Learning
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformation and hallucinations by resorting to at…
MisinformationRetrievalStrategyQAText GenerationCR-LT-KGQA: A Knowledge Graph Question Answering Dataset Requiring Commonsense Reasoning and Long-Tail Knowledge
Knowledge graph question answering (KGQA) is a well-established field that seeks to provide factual answers to natural language (NL) questions by leveraging knowledge graphs (KGs). However, existing KGQA datasets suffer …
Claim VerificationGraph Question AnsweringHallucinationKnowledge Graphs+2Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation
We propose a straightforward approach called Distillation Contrastive Decoding (DCD) to enhance the reasoning capabilities of Large Language Models (LLMs) during inference. In contrast to previous approaches that relied …
Arithmetic ReasoningGSM8KLanguage ModellingQuantization+1Towards Uncertainty-Aware Language Agent
While Language Agents have achieved promising success by placing Large Language Models at the core of a more versatile design that dynamically interacts with the external world, the existing approaches neglect the notion…
MMLUStrategyQAUncertainty QuantificationEscape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning
Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost metho…
GSM8KMathStrategyQA