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Found 12 papers, 6 papers with code
Evaluating Robustness of Reward Models for Mathematical Reasoning
In this work, we introduce a new design for reliable evaluation of reward models, and to validate this, we construct RewardMATH, a benchmark that effectively represents the robustness of reward models in mathematical reasoning tasks.
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing.
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics
The idea of personality in descriptive psychology, traditionally defined through observable behavior, has now been extended to Large Language Models (LLMs) to better understand their behavior.
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
To overcome these limitations, we introduce the BiGGen Bench, a principled generation benchmark designed to thoroughly evaluate nine distinct capabilities of LMs across 77 diverse tasks.
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin.
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
The results suggest that VerifiNER can successfully verify errors from existing models as a model-agnostic approach.
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing.
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning.
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.
TUTORING: Instruction-Grounded Conversational Agent for Language Learners
In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning.
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
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