Search Results for author:
Found 17 papers, 13 papers with code
FormalAlign: Automated Alignment Evaluation for Autoformalization
To address this, we introduce \textsc{FormalAlign}, the first automated framework designed for evaluating the alignment between natural and formal languages in autoformalization.
OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling
Furthermore, to alleviate the data scarcity for optimization problems, and to bridge the gap between open-source LLMs on a small scale (e. g., Llama-3-8b) and closed-source LLMs (e. g., GPT-4), we further propose a data synthesis method namely ReSocratic.
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving
In this paper, we propose FVEL, an interactive Formal Verification Environment with LLMs.
Process-Driven Autoformalization in Lean 4
Autoformalization, the conversion of natural language mathematics into formal languages, offers significant potential for advancing mathematical reasoning.
Proving Theorems Recursively
This approach allows the theorem to be tackled incrementally by outlining the overall theorem at the first level and then solving the intermediate conjectures at deeper levels.
ATG: Benchmarking Automated Theorem Generation for Generative Language Models
We conduct extensive experiments to investigate whether current LMs can generate theorems in the library and benefit the problem theorems proving.
MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data
Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving.
Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation
Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems.
CLOMO: Counterfactual Logical Modification with Large Language Models
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs).
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e. g., mathematical reasoning, commonsense reasoning), and has the potential for further human-machine collaborative scientific findings.
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models.
LEGO-Prover: Neural Theorem Proving with Growing Libraries
Our ablation study indicates that these newly added skills are indeed helpful for proving theorems, resulting in an improvement from a success rate of 47. 1% to 50. 4%.
Ranked #1 on
Automated Theorem Proving
on miniF2F-test
(Pass@100 metric)
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure
To this end, we propose a comprehensive logical reasoning explanation form.
Discourse-Aware Graph Networks for Textual Logical Reasoning
In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs).
DAGN: Discourse-Aware Graph Network for Logical Reasoning
The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks.
Ranked #24 on
Reading Comprehension
on ReClor
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance.
