Papers by Xiang Lorraine Li
Leveraging Large Models to Evaluate Novel Content: A Case Study on Advertisement Creativity (2025.emnlp-main)
Copied to clipboard
| Challenge: | Evaluating creativity is challenging, even for humans, because of its subjectivity and complex cognitive processes. |
| Approach: | They propose a set of tasks to break down visual advertisement creativity into atypicality and originality with fine-grained annotations by humans. |
| Outcome: | The proposed tasks demonstrate the promise and challenges of using VLMs for automated creativity assessment. |
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization (2021.naacl-main)
Copied to clipboard
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula
| Challenge: | Large-scale, open Natural Language Inference datasets have catalyzed the development of NLI models that exhibit close to human-level performance, but the use of these models for other downstream NLP tasks has met with limited success. |
| Approach: | They use multiple-choice reading comprehension and checking factual correctness of textual summarization tasks to investigate potential reasons for this . authors leverage abundance of data from reading comprehension datasets into longer-premise NLI datasets to improve their models . |
| Outcome: | The proposed models outperform models trained on converted datasets due to the difference in premise lengths. |
Neuron-Aware Active Few-Shot Learning for LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods rely on output-level signals for sample identification, such as predictive entropy or semantic similarities with test-time data, which overlook models’ internal dynamics which could pinpoint specific knowledge gaps. |
| Approach: | They propose a Neuron-Aware Active Few-Shot Learning framework that shifts the selection paradigm from output-level proxies to models’ internal dynamics. |
| Outcome: | Experiments on three datasets show that NeuFS outperforms existing AFSL baselines. |
Think Globally, Group Locally: Evaluating LLMs Using Multi-Lingual Word Grouping Games (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models exhibit biases in reasoning abilities due to linguistic modality, even with similar content. |
| Approach: | They propose a task inspired by the New York Times Connections: GlobalGroup that evaluates large language models in an abstract reasoning task across several languages. |
| Outcome: | The proposed task evaluates models across multiple languages in English, Spanish, Chinese, Hindi, and Arabic. |
A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)
Copied to clipboard
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh
| Challenge: | Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. |
| Approach: | They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge. |
| Outcome: | The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance. |
Unveiling Confirmation Bias in Chain-of-Thought Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose to examine how internal beliefs affect reasoning generation and reasoning-guided answer prediction in CoT by decomposing CoT into a two-stage process. |
| Outcome: | The proposed model beliefs affect reasoning generation and reasoning-guided answer prediction in CoT, and the results provide strong evidence of confirmation bias in LLMs. |
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to generalize knowledge bases model triple-level uncertainty . Existing models only model triple level uncertainty, and reasoning results lack global consistency. |
| Approach: | They propose a method to embed knowledge graphs with calibrated probabilistic semantics . they model each entity as a box and relations between two entities as affine transforms based on affinity transforms. |
| Outcome: | Experiments show that the proposed method outperforms baseline methods on confidence prediction and fact ranking. |
Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to update model parameters are limited due to their low efficiency and cost. |
| Approach: | They propose two methods to improve model editing performance by incorporating neighboring knowledge during editing. |
| Outcome: | The proposed methods reduce UnderEdit by 38 percentage points and OverEdit by up to 6 . |