Papers by Shufan Wang
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism (N19-1)
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| Challenge: | Literary critics often attempt to uncover meaning in a single work of literature through careful reading and analysis. |
| Approach: | They propose to use a literary theory to analyze Italo Calvino's novel Invisible Cities to leverage contextualized representations to embed each city's description and use unsupervised methods to cluster embeddings. |
| Outcome: | The proposed method can be applied to Italo Calvino’s novel Invisible Cities . authors compare results to similarity judgments generated by human readers . |
Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
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Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
| Challenge: | Modern large language models perform in-context learning, where query- answer demonstrations are shown before the final query. |
| Approach: | They propose to use in-context learning to prompt queries before they are answered . they find that the choice of demonstrations can affect model performance . |
| Outcome: | The proposed model performance improves on named entity replacements across three reasoning tasks and two popular LLMs. |
Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration (2021.emnlp-main)
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| Challenge: | Phrase representations derived from pretrained language models often lack lexical similarity to determine semantic relatedness. |
| Approach: | They propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings by fine- tuning a dataset of diverse phrasal paraphrases and a large-scale dataset of phrases in context. |
| Outcome: | The proposed model outperforms baseline models across phrase-level similarity tasks while also showing increased lexical diversity between nearest neighbors in the vector space. |
kNN-LM Does Not Improve Open-ended Text Generation (2023.emnlp-main)
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| Challenge: | Interpolation-based retrieval-augmented language models (LMs) are a subtype of retrieval augmented language model that computes the probability of the next token by interpolating between the softmax distribution of the original LM and a token distribution formed by retrieving over an external datastore. |
| Approach: | They propose to interpolate the predicted distribution of the next word with a distribution formed from the most relevant retrievals for a given prefix. |
| Outcome: | The proposed methods do not exhibit improvements in open-ended generation quality, as measured by automatic evaluation metrics and human evaluations. |
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation (2020.emnlp-main)
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| Challenge: | Existing datasets lack rich enough contexts to guide models and evaluations are unreliable for long-form creative text. |
| Approach: | They propose a dataset and evaluation platform built from STORIUM . their dataset contains 6K lengthy stories with fine-grained natural language annotations . |
| Outcome: | The proposed model can be used to generate 6K long stories with fine-grained natural language annotations and a user-generated dataset. |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
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| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing (2023.findings-emnlp)
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| Challenge: | In task-oriented semantic parsing, the system aims to translate users’ utterances in natural language to machine-interpretable programs (API calls) However, Large Language Models (LLMs) are known to hallucinate and therefore pose a formidable challenge in constraining generated content. |
| Approach: | They propose to use large language models to translate user's utterances to machine-interpretable programs (API calls) they identify constraints violations in task-oriented utterrances and define fine-grained metrics that complement traditional ones. |
| Outcome: | The proposed methods reduce constraints violations and improve quality of the generated API calls, but require careful consideration given their implementation complexity and latency. |
AEA: Adaptive Expert Allocation Improves Sentence Embeddings from Mixture-of-Experts LLM (2026.acl-long)
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| Challenge: | Existing methods to improve embeddings from Mixture-of-Experts models allocate a fixed number of experts uniformly across all layers and tokens, ignoring inter-layer and inter-token heterogeneity. |
| Approach: | They propose an Adaptive Expert Allocation framework that performs layer-wise and token-wise expert allocation to enhance embedding quality. |
| Outcome: | The proposed method improves embedding quality across multiple MoE models. |
You can’t pick your neighbors, or can you? When and How to Rely on Retrieval in the kNN-LM (2022.findings-emnlp)
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| Challenge: | Recent studies have shown that retrieval-enhanced language models can improve perplexity by combining text from large external datastores with a k-nearest neighbors model. |
| Approach: | They propose a retrieval-enhanced language model that interpolates existing LMs with a k-nearest neighbors model and requires no additional training. |
| Outcome: | The proposed model improves on two English language modeling datasets and shows that it is most effective when items have high semantic similarity with the query. |
Modeling Exemplification in Long-form Question Answering via Retrieval (2022.naacl-main)
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| Challenge: | Exemplification is a process by which writers explain or clarify a concept by providing an example. |
| Approach: | They propose to use a partially-written answer to query a large set of human-written examples extracted from a corpus to determine exemplification quality. |
| Outcome: | The proposed model is able to retrieve human-written examples from a corpus and show that it is more relevant than state-of-the-art models. |