6 citations · 11 across the 10 of their papers we have counts for
10 papers
Analyzing LLM Instruction Optimization for Tabular Fact Verification
Xiaotang Du, Giwon Hong, Wai-Chung Kwan +4
Instruction optimization provides a lightweight, model-agnostic approach to enhancing the reasoning performance of large language models (LLMs). This paper presents the first syste…
Compositional Steering of Large Language Models with Steering Tokens
Gorjan Radevski, Kiril Gashteovski, Giwon Hong +2
Deploying LLMs in real-world applications requires controllable output that satisfies multiple desiderata at the same time. While existing work extensively addresses LLM steering f…
GRADA: Graph-based Reranking against Adversarial Documents Attack
Jingjie Zheng, Aryo Pradipta Gema, Giwon Hong +4
Retrieval Augmented Generation (RAG) frameworks improve the accuracy of large language models (LLMs) by integrating external knowledge from retrieved documents, thereby overcoming…
Theorem Prover as a Judge for Synthetic Data Generation
Joshua Ong Jun Leang, Giwon Hong, Wenda Li +1
The demand for synthetic data in mathematical reasoning has increased due to its potential to enhance the mathematical capabilities of large language models (LLMs). However, ensuri…
Mixtures of In-Context Learners
Giwon Hong, Emile van Krieken, Edoardo Ponti +2
In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratica…
Analysing the Residual Stream of Language Models Under Knowledge Conflicts
Yu Zhao, Xiaotang Du, Giwon Hong +6
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided…
Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering
Yu Zhao, Alessio Devoto, Giwon Hong +6
Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided…
Are We Done with MMLU?
Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong +13
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates nu…
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Giwon Hong, Aryo Pradipta Gema, Rohit Saxena +8
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, the…
Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
Aryo Pradipta Gema, Giwon Hong, Pasquale Minervini +2
The NLI4CT task assesses Natural Language Inference systems in predicting whether hypotheses entail or contradict evidence from Clinical Trial Reports. In this study, we evaluate v…