activity
20172026
most citedEvaluating Psychological Safety of Large Language Models

26 citations · 110 across the 20 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CL2023★ 16 cited

ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?

Hailin Chen, Fangkai Jiao, Xingxuan Li +5

Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce. Through instruction-tuning a large language model (…

cs.CL2023★ 2 cited

Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework

Ruochen Zhao, Xingxuan Li, Shafiq Joty +2

As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of fa…

cs.CL2023★ 5 cited

Is GPT-4 a Good Data Analyst?

Liying Cheng, Xingxuan Li, Lidong Bing

As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation,…

cs.CL2023★ 5 cited

Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic

Xingxuan Li, Liying Cheng, Qingyu Tan +3

The temporal aspect is a significant dimension of our reality. We notice the challenge that large language models (LLMs) face when engaging in temporal reasoning. Our preliminary e…

cs.CL2023★ 18 cited

Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

Xingxuan Li, Ruochen Zhao, Yew Ken Chia +4

We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It…

cs.CL2023★ 15 cited

Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines

Ruochen Zhao, Xingxuan Li, Yew Ken Chia +2

Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, techno…

cs.CL2023★ 3 cited

Retrieving Multimodal Information for Augmented Generation: A Survey

Ruochen Zhao, Hailin Chen, Weishi Wang +8

As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better intera…