Papers by Simone Filice
VoiSeR: A New Benchmark for Voice-Based Search Refinement (2021.eacl-main)
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| Challenge: | a new study shows that voice-based search systems are challenging to support in the context of the user intent of voice searches . support for voice-driven search, exploration, and refinement is a fundamental aspect of voice assistants . |
| Approach: | They propose to use crowdsourcing to collect voice-based search refinements . they use 10,000 search refinement utterances to annotate a search intent . |
| Outcome: | The proposed dataset shows that voice-based search refinements can support most common tasks . the study shows that the proposed dataset can support research in conversational query understanding . |
The Distracting Effect: Understanding Irrelevant Passages in RAG (2025.acl-long)
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| Challenge: | Existing methods to detect and use hard distracting passages in RAG can cause problems . retrieved passages contain irrelevant but semantically related information that may mislead the LLM . |
| Approach: | They propose a method to identify and use hard distracting passages to improve RAG . they find that adding retrieved passages is found to ground the LLM response . |
| Outcome: | The proposed method achieves up to 7.5% increase in answering accuracy compared to fine-tuned datasets. |
Do RAG Systems Really Suffer From Positional Bias? (2025.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
| Approach: | They propose to use a retrieval algorithm to add passages from an external corpus to the LLM prompt to improve the factual accuracy of LLMs. |
| Outcome: | The proposed approach improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
Learning to Generate Examples for Semantic Processing Tasks (2022.naacl-main)
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| Challenge: | Recent data augmentation techniques can help to deal with low resource settings, such as BERT, but they can hurt the results. |
| Approach: | They propose a neural approach to automatically learn to generate new examples using a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed approach outperforms existing methods on text classification and natural language inference tasks by 10%. |
Redefining Retrieval Evaluation in the Era of LLMs (2026.eacl-long)
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| Challenge: | Traditional IR metrics assume that humans examine documents sequentially with diminishing attention to lower ranks. |
| Approach: | They propose a utility-based annotation schema that quantifies positive contribution of relevant passages and negative impact of distracting ones. |
| Outcome: | The proposed metric improves correlation with the end-to-end answer accuracy by up to 36% compared to traditional metrics. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
Generating Q&A Benchmarks for RAG Evaluation in Enterprise Settings (2025.acl-industry)
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| Challenge: | DataMorgana generates synthetic Q&A benchmarks tailored to RAG applications . lexical, syntactic, and semantic diversity of generated benchmarks exceeds existing tools . |
| Approach: | They propose a tool for generating synthetic Q&A benchmarks tailored to RAG applications in enterprise settings. |
| Outcome: | The proposed tool surpasses existing tools in terms of lexical, syntactic, and semantic diversity while maintaining high quality. |
Learning to Solve NLP Tasks in an Incremental Number of Languages (2021.acl-short)
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| Challenge: | Existing models that support multiple languages are expensive and time-consuming to retrain . retraining only with the new language data will result in Catastrophic Forgetting of previously acquired knowledge. |
| Approach: | They propose a Continual Learning strategy that updates a model to support new languages over time while maintaining consistent results on previously learned languages. |
| Outcome: | The proposed model is based on an existing model that "teaches" to a student while the student is also trained on a new language. |
Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) operating in 0-shot or few-shot settings achieve competitive results in Text Classification tasks. |
| Approach: | They propose to make Large Language Models (LLMs) operating in 0-shot or few-shot settings as efficient as 0- shot text classifiers by leveraging a small number of samples. |
| Outcome: | The proposed model is able to perform better on multiple datasets than existing models on 0-shot or few-shot settings. |