Papers by Linlu Qiu
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024.naacl-long)
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Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim
| Challenge: | Recent language models possess impressive performance across a wide range of tasks . however, they often rely on narrow, non-transferable procedures for task-solving . |
| Approach: | They propose to evaluate language models using "counterfactual" task variants that deviate from standard tasks. |
| Outcome: | The proposed framework shows that language models perform better on a wide range of tasks compared to the default conditions. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? (2021.emnlp-main)
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| Challenge: | a general-purpose Transformer-based model with crossmodal attention solves most of the systematic generalization problems . current models are data inefficient given the narrow scope of commands in gSCAN . |
| Approach: | They propose to use a Transformer-based model with cross-modal attention to solve gSCAN . they propose to generate data to incorporate relations between objects in the visual environment . |
| Outcome: | The proposed model outperforms specialized approaches on most splits, and is data inefficient given the narrow scope of commands. |
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)
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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova
| Challenge: | Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling . |
| Approach: | They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods . |
| Outcome: | The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds . |
On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts (2025.emnlp-main)
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| Challenge: | Language models (LMs) are increasingly used as conversational agents because of their pragmatic reasoning abilities. |
| Approach: | They propose an evaluation framework derived from *Wavelength*, a popular communication game where a speaker and a listener communicate about a broad range of concepts in a granular manner. |
| Outcome: | The proposed evaluation framework outperforms direct and Chain-of-Thought (CoT) prompting on language comprehension and language production tasks. |
Visually Grounded Concept Composition (2021.findings-emnlp)
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| Challenge: | Existing approaches to visual grounding do not explicitly model compositional structures of text expressions. |
| Approach: | They propose a concept-relation Graph and a composition neural network to combine CRGs . they propose to align CRG-based concepts with images to learn visually grounded concepts . |
| Outcome: | The proposed model can model grounded concepts forming at sentence level and word level. |
Implicit Representations of Grammaticality in Language Models (2026.acl-long)
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| Challenge: | Pretrained language models generate grammatically well-formed text and discriminate well between grammatical and ungrammatically sentences in tightly controlled minimal pairs. |
| Approach: | They propose a method to train pretrained LMs for representations of grammaticality by applying perturbations to a naturalistic text corpus. |
| Outcome: | The proposed model outperforms probability-based models on human-curated grammaticality judgment benchmarks and performs worse than string probabilities on plausibility benchmarks. |
Improving Compositional Generalization with Latent Structure and Data Augmentation (2022.naacl-main)
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| Challenge: | Generic unstructured neural networks struggle on out-of-distribution compositional generalization. |
| Approach: | They propose a method to recombinate examples from a model called Compositional Structure Learner and add them to a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed model is even stronger than a T5-CSL ensemble on two real world compositional generalization tasks. |
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps (2024.emnlp-main)
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| Challenge: | Despite the utility and impressive capabilities of large language models, their tendency to generate hallucinations presents a significant challenge in their deployment. |
| Approach: | They propose a simple hallucination detection model based on the ratio of attention weights on the context versus newly generated tokens. |
| Outcome: | The proposed model reduces the amount of hallucinations by 9.6% in a summarization task. |