Papers by Luis Lastras
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs (2023.emnlp-main)
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| Challenge: | Existing metrics for faithfulness of response are not aligned with human judgments. |
| Approach: | They propose a new metric that utilizes (Conditional) Point-wise Mutual Information (PMI) between the generated response and the source document, conditioned on the dialogue. |
| Outcome: | The proposed metric improves on BEGIN benchmarks and shows that it generates more faithful responses than standard decoding techniques. |
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset (2020.emnlp-main)
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| Challenge: | doc2dial dataset is a goal-oriented document-grounded dialogue model . it is based on how the authors compose documents for guiding end users . |
| Approach: | They propose a dataset of goal-oriented dialogues grounded in documents . they use annotated conversations with an average of 14 turns to generate conversational utterances . |
| Outcome: | The proposed dataset includes over 4500 annotated conversations with an average of 14 turns grounded in over 450 documents from four domains. |
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks (2024.emnlp-industry)
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Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G P Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Soria, Dheeraj Sreedhar, Praveen Venkateswaran, Merve Unuvar, David Cox, Salim Roukos, Luis Lastras, Pavan Kapanipathi
| Challenge: | Existing research explores the use of Large Language Models (LLMs) as the backbone of agentic systems. |
| Approach: | They propose a model trained using a multi-task training approach on seven fundamental tasks encompassed in function calling that has better generalizability on multiple tasks across seven evaluation benchmarks. |
| Outcome: | The proposed model outperforms more than 15 other models on out-of-domain datasets and ranks among the top on the Berkeley Function Calling Leaderboard (BFCL). |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
Conversational Document Prediction to Assist Customer Care Agents (2020.emnlp-main)
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Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using a conversational search system, the agent/system can ask clarification questions and interactively modify the search results as the conversation progresses. |
| Approach: | They propose to use a public dataset to analyze the task of predicting the documents that customer care agents can use to facilitate users’ needs. |
| Outcome: | The proposed model is more efficient than existing models and is more cost-effective than existing ones. |
Does Structure Matter? Encoding Documents for Machine Reading Comprehension (2021.naacl-main)
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| Challenge: | Existing Transformer-based models for machine reading comprehension treat documents as flat sequences. |
| Approach: | They propose a Transformer-based method that reads a document as tree slices and jointly trains and consults the modules at inference time. |
| Outcome: | The proposed method outperforms several baseline approaches on two datasets from varied domains. |
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020.acl-main)
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| Challenge: | Lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in literature. |
| Approach: | They propose an improved evaluation protocol for implicit relation classification on PDTB 2.0 . they report strong baseline results from pretrained sentence encoders . |
| Outcome: | The proposed evaluation protocol improves the existing framework and provides strong baseline results. |
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs (2024.acl-long)
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Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Vernon Austel, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis Lastras
| Challenge: | Existing methods to train and test large language models that involve calls to tools and APIs are lacking. |
| Approach: | They propose a large corpora for training and systematic testing of tool-augmented LLMs. |
| Outcome: | The proposed datasets mimic real-world scenarios involving API-tasks and slot filling. |