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Logical Reasoning

Papers

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Showing 501–550 of 747 papers

TitleStatusHype
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models—0
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical ReasoningCode0
Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case StudyCode0
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models—0
Are LLMs Rigorous Logical Reasoner? Empowering Natural Language Proof Generation with Contrastive Stepwise Decoding—0
Language Models can be Logical Solvers—0
Let's Reinforce Step by Step—0
COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System—0
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions—0
Rule Learning as Machine Translation using the Atomic Knowledge BankCode0
Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral AnalysisCode0
Generating by Understanding: Neural Visual Generation with Logical Symbol GroundingsCode0
POE: Process of Elimination for Multiple Choice ReasoningCode0
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-AttentionCode0
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism—0
DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions—0
Retrieval-Augmented Neural Response Generation Using Logical Reasoning and Relevance Scoring—0
The potential of large language models for improving probability learning: A study on ChatGPT3.5 and first-year computer engineering students—0
Empower Nested Boolean Logic via Self-Supervised Curriculum LearningCode0
DecoderLens: Layerwise Interpretation of Encoder-Decoder TransformersCode0
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot PerformanceCode0
Learning Reliable Logical Rules with SATNet—0
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models—0
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks—0
Physics of Language Models: Part 3.2, Knowledge Manipulation—0
EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context LearningCode0
On the Potential of CLIP for Compositional Logical Reasoning—0
LR-XFL: Logical Reasoning-based Explainable Federated LearningCode0
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks—0
Deciphering Raw Data in Neuro-Symbolic Learning with Provable GuaranteesCode0
How susceptible are LLMs to Logical Fallacies?Code0
Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought—0
Learning the meanings of function words from grounded language using a visual question answering modelCode0
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals—0
Structural Embeddings of Tools for Large Language Models—0
Is ChatGPT a Good Personality Recognizer? A Preliminary Study—0
Meta-Reasoning: Semantics-Symbol Deconstruction for Large Language ModelsCode0
What is the Title of this Paper? Solving logic puzzles using algorithms—0
Counterfactual Collaborative Reasoning—0
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion—0
Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases—0
Language to Rewards for Robotic Skill Synthesis—0
V-LoL: A Diagnostic Dataset for Visual Logical LearningCode0
Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence—0
Human-in-the-Loop through Chain-of-Thought—0
LogiQA 2.0—An Improved Dataset for Logical Reasoning in Natural Language UnderstandingCode0
ChatGPT is a Remarkable Tool -- For Experts—0
Knowledge-based Reasoning and Learning under Partial Observability in Ad Hoc Teamwork—0
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual IllusionCode0
Synthesizing a Progression of Subtasks for Block-Based Visual Programming TasksCode0
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All datasetsLingOlyBIG-bench (Formal Fallacies Syllogisms Negation)BIG-bench (Penguins In A Table)BIG-bench (Reasoning About Colored Objects)BIG-bench (Temporal Sequences)BIG-bench (Logic Grid Puzzle)BIG-bench (StrategyQA)RuWorldTreeWinograd AutomaticBIG-bench (Logical Fallacy Detection)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Claude OpusDelta_NoContext28.8—Unverified
2GPT-4oDelta_NoContext25.1—Unverified
3Gemini 1.5 ProDelta_NoContext23.4—Unverified
4GPT-4Delta_NoContext21.5—Unverified
5Command R+Delta_NoContext11.6—Unverified
6GPT-3.5Delta_NoContext11.2—Unverified
7Mixtral 8x7BDelta_NoContext6.4—Unverified
8Llama 3 8BDelta_NoContext4.9—Unverified
9Llama 3 70BDelta_NoContext2.9—Unverified
10Gemma 7BDelta_NoContext2.2—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, Direct)Accuracy64.8—Unverified
2PaLM 2 (few-shot, k=3, CoT)Accuracy57.2—Unverified
3OPT 66B (few-shot, k=3)Accuracy54—Unverified
4PaLM 540B (few-shot, k=3)Accuracy53.6—Unverified
5GPT-NeoX 20B (few-shot, k=3)Accuracy52.8—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy52.8—Unverified
7Chinchilla-70B (few-shot, k=5)Accuracy52.1—Unverified
8Bloomberg GPT 50B (few-shot, k=3)Accuracy50.8—Unverified
9Gopher-280B (few-shot, k=5)Accuracy50.7—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy84.9—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy65.8—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy48.7—Unverified
4PaLM 540B (few-shot, k=3)Accuracy44.5—Unverified
5Gopher-280B (few-shot, k=5)Accuracy40.6—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy40.41—Unverified
7Bloomberg GPT (few-shot, k=3)Accuracy37.67—Unverified
8GPT-NeoX (few-shot, k=3)Accuracy33.56—Unverified
9OPT 66B (few-shot, k=3)Accuracy28.08—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy91.2—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy61.2—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy59.7—Unverified
4Gopher-280B (few-shot, k=5)Accuracy49.2—Unverified
5PaLM 540B (few-shot, k=3)Accuracy38—Unverified
6BLOOM 176B (few-shot, k=3)Accuracy36.8—Unverified
7Bloomberg GPT (few-shot, k=3)Accuracy34.8—Unverified
8OPT 66B (few-shot, k=3)Accuracy31.2—Unverified
9GPT-NeoX (few-shot, k=3)Accuracy26—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM 2 (few-shot, k=3, CoT)Accuracy100—Unverified
2PaLM 2 (few-shot, k=3, Direct)Accuracy96.4—Unverified
3PaLM 540B (few-shot, k=3)Accuracy39.6—Unverified
4BLOOM 176B (few-shot, k=3)Accuracy36.8—Unverified
5Chinchilla-70B (few-shot, k=5)Accuracy32—Unverified
6Bloomberg GPT (few-shot, k=3)Accuracy29.2—Unverified
7OPT 66B (few-shot, k=3)Accuracy23.6—Unverified
8GPT-NeoX (few-shot, k=3)Accuracy21.2—Unverified
9Gopher-280B (few-shot, k=5)Accuracy19—Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy44—Unverified
2PaLM-540B (few-shot, k=5)Accuracy42.4—Unverified
3PaLM-62B (few-shot, k=5)Accuracy36.5—Unverified
4Gopher-280B (few-shot, k=5)Accuracy35.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLM-540B (few-shot, k=5)Accuracy73.9—Unverified
2Chinchilla-70B (few-shot, k=5)Accuracy68.3—Unverified
3PaLM-62B (few-shot, k=5)Accuracy65.4—Unverified
4Gopher-280B (few-shot, k=5)Accuracy61—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy 83.7—Unverified
2RuGPT-3 LargeAccuracy 40.7—Unverified
3RuGPT-3 MediumAccuracy 38—Unverified
4RuGPT-3 SmallAccuracy 34—Unverified
#ModelMetricClaimedVerifiedStatus
1Human benchmarkAccuracy87—Unverified
2RuGPT-3 SmallAccuracy57.9—Unverified
3RuGPT-3 MediumAccuracy57.2—Unverified
4RuGPT-3 LargeAccuracy55.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy72.1—Unverified
2Gopher-280B (few-shot, k=5)Accuracy58.9—Unverified