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Logical Reasoning

Papers

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Showing 551–600 of 747 papers

TitleStatusHype
Town Hall Debate Prompting: Enhancing Logical Reasoning in LLMs through Multi-Persona Interaction—0
Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs—0
Transformer-based Language Models for Reasoning in the Description Logic ALCQ—0
Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests—0
Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness—0
A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables—0
TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Games—0
Type-dependent Prompt CycleQAG : Cycle Consistency for Multi-hop Question Generation—0
Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation—0
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning—0
Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator—0
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring—0
VERUS-LM: a Versatile Framework for Combining LLMs with Symbolic Reasoning—0
VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models—0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge—0
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL—0
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation—0
What is the Title of this Paper? Solving logic puzzles using algorithms—0
What Makes Machine Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types—0
Why should we ever automate moral decision making?—0
XAgents: A Framework for Interpretable Rule-Based Multi-Agents Cooperation—0
WatME: Towards Lossless Watermarking Through Lexical Redundancy—0
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning—0
Logic Pre-Training of Language Models—0
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models—0
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training—0
LoNLI: An Extensible Framework for Testing Diverse Logical Reasoning Capabilities for NLI—0
Lp : A Logic for Statistical Information—0
Intermediate Languages Matter: Formal Choice Drives Neurosymbolic LLM Reasoning—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
Mapping Ontologies Using Ontologies: Cross-lingual Semantic Role Information Transfer—0
MARCO: Meta-Reflection with Cross-Referencing for Code Reasoning—0
MathDivide: Improved mathematical reasoning by large language models—0
Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning—0
Medical idioms for clinical Bayesian network development—0
MediSee: Reasoning-based Pixel-level Perception in Medical Images—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
MetaReflection: Learning Instructions for Language Agents using Past Reflections—0
MiCo: Multi-image Contrast for Reinforcement Visual Reasoning—0
Mind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning—0
Mixed Logical and Probabilistic Reasoning for Planning and Explanation Generation in Robotics—0
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs—0
Modeling Associative Reasoning Processes—0
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention—0
Modeling Human Decision-making: An Overview of the Brussels Quantum Approach—0
Motion-R1: Chain-of-Thought Reasoning and Reinforcement Learning for Human Motion Generation—0
MTMT: Consolidating Multiple Thinking Modes to Form a Thought Tree for Strengthening LLM—0
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest—0
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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