Search Results for author: Lifan Yuan

Found 10 papers, 8 papers with code

Prudent Silence or Foolish Babble? Examining Large Language Models' Responses to the Unknown

no code implementations16 Nov 2023 Genglin Liu, Xingyao Wang, Lifan Yuan, Yangyi Chen, Hao Peng

When presented with such unanswerable questions, an LLM should appropriately convey uncertainty, and be able to challenge the premise and refuse to generate a response.

Question Answering valid

UltraFeedback: Boosting Language Models with High-quality Feedback

1 code implementation2 Oct 2023 Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, Maosong Sun

However, the scarcity of diverse, naturalistic datasets of human preferences on LLM outputs at scale poses a great challenge to RLHF as well as feedback learning research within the open-source community.

Language Modelling

CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets

1 code implementation29 Sep 2023 Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi R. Fung, Hao Peng, Heng Ji

It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks.

Language Modelling Mathematical Reasoning

MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback

no code implementations19 Sep 2023 Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, Heng Ji

However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users.

Decision Making

Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations

1 code implementation7 Jun 2023 Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, Maosong Sun

Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets.

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

1 code implementation29 May 2023 Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework.

Adversarial Attack

A Close Look into the Calibration of Pre-trained Language Models

1 code implementation31 Oct 2022 Yangyi Chen, Lifan Yuan, Ganqu Cui, Zhiyuan Liu, Heng Ji

We observe a consistent change in calibration performance across six factors.

A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks

1 code implementation17 Jun 2022 Ganqu Cui, Lifan Yuan, Bingxiang He, Yangyi Chen, Zhiyuan Liu, Maosong Sun

However, we highlight two issues in previous backdoor learning evaluations: (1) The differences between real-world scenarios (e. g. releasing poisoned datasets or models) are neglected, and we argue that each scenario has its own constraints and concerns, thus requires specific evaluation protocols; (2) The evaluation metrics only consider whether the attacks could flip the models' predictions on poisoned samples and retain performances on benign samples, but ignore that poisoned samples should also be stealthy and semantic-preserving.

text similarity

Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework

1 code implementation28 Oct 2021 Lifan Yuan, Yichi Zhang, Yangyi Chen, Wei Wei

In this paper, we instantiate our framework with an attack algorithm named Textual Projected Gradient Descent (T-PGD).

Adversarial Attack Language Modelling

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