Meta-reasoning: Semantics-symbol deconstruction for large language models
Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities
of large language models (LLMs). However, existing methods mainly rely on syntactically …
of large language models (LLMs). However, existing methods mainly rely on syntactically …
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset
of 137K instruction-following demonstrations for 54 tasks covering five essential scientific …
of 137K instruction-following demonstrations for 54 tasks covering five essential scientific …
Focus On This, Not That! Steering LLMs With Adaptive Feature Specification
Despite the success of Instruction Tuning (IT) in training large language models (LLMs) to
perform arbitrary user-specified tasks, these models often still leverage spurious or biased …
perform arbitrary user-specified tasks, these models often still leverage spurious or biased …
EXCGEC: A Benchmark of Edit-wise Explainable Chinese Grammatical Error Correction
Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited
scenario, where they ignore the interaction between corrections and explanations. To bridge …
scenario, where they ignore the interaction between corrections and explanations. To bridge …
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety
of complex tasks. One significant application of LLMs is in tackling software engineering …
of complex tasks. One significant application of LLMs is in tackling software engineering …