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Found 12 papers, 10 papers with code
An Empirical Analysis of Uncertainty in Large Language Model Evaluations
As LLM-as-a-Judge emerges as a new paradigm for assessing large language models (LLMs), concerns have been raised regarding the alignment, bias, and stability of LLM evaluators.
Outcome-Refining Process Supervision for Code Generation
Large Language Models have demonstrated remarkable capabilities in code generation, yet they often struggle with complex programming tasks that require deep algorithmic reasoning.
CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification
In this study, we propose CodePurify, a novel defense against backdoor attacks on code models through entropy-based purification.
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency.
CodeShell Technical Report
Through this process, We have curated 100 billion high-quality pre-training data from GitHub.
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness.
Supervised Knowledge Makes Large Language Models Better In-context Learners
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).
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ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models
In this paper, we present a large-scale empirical study on general language ability evaluation of PLMs (ElitePLM).
TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.
