Papers by Adian Liusie
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models (2024.eacl-long)
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| Challenge: | Recent advances in large language models have enabled impressive zero-shot capabilities across various natural language tasks. |
| Approach: | They propose two ways to exploit the emergent abilities of large language models for NLG assessment. |
| Outcome: | The proposed methods improve performance and positional biases in comparisons between candidates. |
WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models (2024.findings-naacl)
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| Challenge: | Recent work has shown that small, context-dependent shifts in word distributions can be used to apply and detect watermarks, but little work has analyzed the impact of these perturbations on the quality of generated texts. |
| Approach: | They propose a framework that allows for analysis of the impact of watermark settings on the quality of generated texts. |
| Outcome: | The proposed framework provides easy visualization of the quality-detection trade-off of watermark settings. |
Investigating the Emergent Audio Classification Ability of ASR Foundation Models (2024.naacl-long)
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| Challenge: | Text and vision foundation models can perform many tasks in a zero-shot setting . however, there has been little work on the zero-shoot abilities of ASR foundation models . |
| Approach: | They investigate the ability of ASR foundation models to perform zero-shot audio classification using text prompts and a decoding probability generator. |
| Outcome: | The proposed model outperforms state-of-the-art models on audio classification datasets without training them on extra data or adding any parameters. |
Efficient LLM Comparative Assessment: A Product of Experts Framework for Pairwise Comparisons (2024.emnlp-main)
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| Challenge: | LLM-as-a-judge approaches are effective but cost scales quadratically with number of candidates, which has practical limitations. |
| Approach: | They propose a Product of Expert (PoE) framework for efficient LLM Comparative Assessment where individual comparisons are considered experts that provide information on a pair’s score difference. |
| Outcome: | The proposed framework can generate score predictions that correlate well with human judgements on multiple NLG tasks with as few as 2% of comparisons. |
Who Needs Decoders? Efficient Estimation of Sequence-Level Attributes with Proxies (2024.eacl-long)
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| Challenge: | Autoregressive decoding is expensive for many sequence-to-sequence tasks, but for some downstream tasks, the actual decoding output is not needed, just attributes of the sequence. |
| Approach: | They propose non-autoregressive proxy models that can efficiently predict scalar-valued sequence-level attributes from the encodings, avoiding the expensive decoding stage. |
| Outcome: | The proposed models outperform ensembles in machine translation (MT) and automatic speech recognition (ASR) while being significantly faster. |
Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they often fail to maintain crucial invariances for specific tasks. |
| Approach: | They propose to distill the capabilities of a computationally intensive, debiased, teacher model into a more compact student model. |
| Outcome: | The proposed model outperforms larger, biased teacher models by reducing the invariances of the student model by incorporating encoders. |
Finetuning LLMs for Comparative Assessment Tasks (2025.coling-main)
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| Challenge: | Automated assessment in natural language generation is a challenging task. |
| Approach: | They propose a framework for fine-tuning LLMs for comparative assessment to align the model’s output with the target distribution of comparative probabilities. |
| Outcome: | The proposed framework improves state-of-the-art performance while maintaining high performance with an efficient subset of comparisons. |
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (2023.emnlp-main)
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| Challenge: | Existing fact-checking approaches require access to external databases or external databases . a lack of external databases can undermine trust in large language models. |
| Approach: | They propose a sampling-based approach to fact-check black-box models without external databases. |
| Outcome: | The proposed approach can be used to fact-check black-box models without external databases . it can detect non-factual and factual sentences and rank passages in terms of factuality . |
Analyzing Biases to Spurious Correlations in Text Classification Tasks (2022.aacl-short)
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| Challenge: | Often these systems exceed human performance, but there is a caveat: standard benchmarks often assume that training and evaluation data are drawn independently and identically from the same underlying distribution. |
| Approach: | They propose to exploit spurious correlations in training data to exploit these correlations . they show that even when only ‘stop’ words are available, it is possible to predict the class significantly better than random. |
| Outcome: | The proposed model can predict class significantly better when only ‘stop’ words are available at the input stage, but can degrade the ability of the system to generalize well to out-of-domain data. |
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are powerful zero-shot assessors used in real-world situations . however, no study has examined the vulnerability of judge-LLM to adversarial manipulation . |
| Approach: | They propose a simple surrogate attack where a surrogated model is attacked and the learned attack phrase transferred to unknown judge-LLMs. |
| Outcome: | The proposed algorithm shows that judge-LLMs can be significantly more susceptible to adversarial attacks when used for absolute scoring, rather than comparative assessment. |