We present ALT (ALignment with Textual feedback), an approach that aligns language models with user preferences expressed in text.We argue that text offers greater expressiveness, enabling users to provide richer feedback than simple comparative preferences, leading to more efficient and effective alignment.ALT aligns the model by conditioning its generations on the textual feedback.Our method relies solely on language modeling techniques and requires minimal hyper-parameter tuning while retaining the main benefits of RL-based alignment algorithms.We demonstrate the efficacy and efficiency of textual feedback across different tasks, including toxicity reduction, summarization, and dialogue response generation.Notably, ALT outperforms PPO in toxicity reduction and matches its performance on summarization with only 20% of the samples.We also explore using ALT with feedback from an existing LLM, examining constrained and unconstrained feedback.Additionally, we outline future directions to align models with natural language feedback.1

Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts are however often only available within full-text event descriptions from newspapers and other reports. Extracting numbers from text is challenging: numbers have different formats and may require numeric reasoning. This renders purely tagging approaches insufficient. As a consequence, fine-grained counts of injured, displaced, or abused victims beyond fatalities are often not extracted and remain unseen. We cast victim count extraction as a question answering (QA) task with a regression or classification objective. We compare tagging approaches: regex, dependency parsing, semantic role labeling, and advanced text-to-text models. Beyond model accuracy, we analyze extraction reliability and robustness which are key for this sensitive task. In particular, we discuss model calibration and investigate out-of-distribution and few-shot performance. Ultimately, we make a comprehensive recommendation on which model to select for different desiderata and data domains. Our work is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact.

Vilém Zouhar, Shehzaad Dhuliawala, Wangchunshu Zhou, Nico Daheim, Tom Kocmi, Yuchen Eleanor Jiang, Mrinmaya Sachan. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

In a human-AI collaboration, users build a mental model of the AI system based on its reliability and how it presents its decision, e.g. its presentation of system confidence and an explanation of the output.Modern NLP systems are often uncalibrated, resulting in confidently incorrect predictions that undermine user trust.In order to build trustworthy AI, we must understand how user trust is developed and how it can be regained after potential trust-eroding events.We study the evolution of user trust in response to these trust-eroding events using a betting game.We find that even a few incorrect instances with inaccurate confidence estimates damage user trust and performance, with very slow recovery.We also show that this degradation in trust reduces the success of human-AI collaboration and that different types of miscalibration-unconfidently correct and confidently incorrect-have different negative effects on user trust.Our findings highlight the importance of calibration in user-facing AI applications and shed light on what aspects help users decide whether to trust the AI system.

Abstract In a conversational question answering scenario, a questioner seeks to extract information about a topic through a series of interdependent questions and answers. As the conversation progresses, they may switch to related topics, a phenomenon commonly observed in information-seeking search sessions. However, current datasets for conversational question answering are limiting in two ways: 1) they do not contain topic switches; and 2) they assume the reference text for the conversation is given, that is, the setting is not open-domain. We introduce TopiOCQA (pronounced Tapioca), an open-domain conversational dataset with topic switches based on Wikipedia. TopiOCQA contains 3,920 conversations with information-seeking questions and free-form answers. On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents). TopiOCQA poses a challenging test-bed for models, where efficient retrieval is required on multiple turns of the same conversation, in conjunction with constructing valid responses using conversational history. We evaluate several baselines, by combining state-of-the-art document retrieval methods with neural reader models. Our best model achieves F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of our dataset. Our dataset and code are available at https://mcgill-nlp.github.io/topiocqa.

WordNet has proved to be immensely useful for Word Sense Disambiguation, and thence Machine translation, Information Retrieval and Question Answering.It can also be used as a dictionary for educational purposes.The semantic nature of concepts in a Word-Net motivates one to try to express this meaning in a more visual way.In this paper, we describe our work of enriching IndoWordNet with image acquisitions from the OpenClipArt library.We describe an approach used to enrich WordNets for eighteen Indian languages.Our contribution is three fold: (1) We develop a system, which, given a synset in English, finds an appropriate image for the synset.The system uses the OpenclipArt library (OCAL) to retrieve images and ranks them.(2) After retrieving the images, we map the results along with the linkages between Princeton WordNet and Hindi Word-Net, to link several synsets to corresponding images.We choose and sort top three images based on our ranking heuristic per synset.(3) We develop a tool that allows a lexicographer to manually evaluate these images.The top images are shown to a lexicographer by the evaluation tool for the task of choosing the best image representation.The lexicographer also selects the number of relevant images.Using our system, we obtain an Average Precision (P @ 3) score of 0.30.