Test of Time Awards

Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection

Presidential Address

A Fine-grained citation graph for biomedical academic papers: the finding-citation graph

What Makes Language Models Good-enough?

One of the frequent points in the mainstream narrative about large language models is that they have “emergent properties” (sometimes even dangerous enough to be considered existential risk to mankind). However, there is much disagreement about even the very definition of such properties. If they are understood as a kind of generalization beyond training data - as something that a model does without being explicitly trained for it - I argue that we have not in fact established the existence of any such properties, and at the moment we do not even have the methodology for doing so.

A sanity check on emergent properties

Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling humans to specify, through language, exactly how an agent should complete a task (i.e., low-level specification), prior work lacks on interpreting the high-level strategic intent of the human commanders. Parsing strategic intent from language will allow autonomous systems to independently operate according to the user's plan without frequent guidance or instruction. In this paper, we build a computational interface capable of translating unstructured language strategies into actionable intent in the form of goals and constraints. Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i.e., goals and constraints) from language (p < 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p < 0.05) in a low-data setting.

A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting | VIDEO

The prevalence and salience of LMs has changed the way many researchers in NLP think about our work, causing many issues for model evaluation. This presentation presents a retrospective of recent past approaches to modeling text, and discusses what has changed, and what the current evaluation challenges are. Next, it discusses common pitfalls that LM-based research falls into, highlighting several fallacies, and suggesting ways to avoid them. Finally, this presentation highlights the importance of meta-evaluation research (the evaluation of our evaluations) as one crucial component to overcoming LM evaluation challenges in the post LM-boom.

Evaluation after the LM boom: Frustrations, fallacies andthe future

Oral Presentations of Contextual Bangla Neural Stemmer: Finding Contextualized Root Word Representations for Bangla Words

Oral Presentation of CNS Model-Paper ID: 68 | VIDEO

Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability and the instability of implicit reasoning, particularly when both planning and execution are involved. To overcome these limitations, we propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization. CREATOR disentangles abstract tool creation and concrete decision execution, resulting in improved performance. We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems and diverse tabular contents. Remarkably, CREATOR outperforms existing chain-of-thought, program-of-thought, and tool-using baselines. Additionally, we introduce the Creation Challenge dataset, featuring 2K diverse questions, to emphasize the necessity and benefits of LLMs' tool creation ability. Further research demonstrates that leveraging LLMs as tool creators facilitates knowledge transfer, and LLMs exhibit varying levels of tool creation abilities, enabling them to adapt to diverse situations. The tool creation ability revolutionizes the LLM's problem-solving paradigm, driving us closer to the next frontier of artificial intelligence.

CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models

Natural language serves as the primary mode of communication when an intelligent agent with a physical presence engages with human beings. While a plethora of research focuses on natural language understanding (NLU), encompassing endeavors such as sentiment analysis, intent prediction, question answering, and summarization, the scope of NLU directed at situations necessitating tangible actions by an embodied agent remains limited. The inherent ambiguity and incompleteness inherent in natural language present challenges for intelligent agents striving to decipher human intention. To tackle this predicament head-on, we introduce a novel system known as \textbf{t}ask and \textbf{a}rgument \textbf{g}rounding for \textbf{E}mbodied agents (\system). At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions. These extracted tasks are then mapped (or grounded) to the robot's established collection of skills, while the arguments find grounding in objects present within the environment. To facilitate the training and evaluation of our system, we have curated a dataset featuring complex instructions. The results of our experiments underscore the prowess of our approach, as it outperforms robust baseline models.

tagE: Enabling an Embodied Agent to Understand Human Instructions​

Inverse Text Normalization (ITN) involves rewriting the verbalised form of text from spoken transcripts to its corresponding written form. The task inherently expects challenges in identifying ITN entries due to spelling variations in words arising out of dialects, transcription errors etc. Additionally, in Tamil, word boundaries between adjacent words in a sentence often get obscured due to Punarchi, i.e. phonetic transformation of these boundaries. Being morphologically rich, the words in Tamil show a high degree of agglutination due to inflection and clitics. The combination of such factors leads to a high degree of surfaceform variations, making scalability with pure rule-based approaches difficult. Instead, we experiment with fine-tuning three pre-trained neural LMs, consisting of a seq2seq model (s2s), a non-autoregressive text editor (NAR) and a sequence tagger + rules combination (tagger). While the tagger approach works best in a fully-supervised setting, s2s performs the best (98.05 F-Score) when augmented with additional data, via bootstrapping and data augmentation (DA&B). S2S reports a cumulative percentage improvement of 20.1 %, and statistically significant gains for all our models with DA&B. Compared to a fully supervised setup, bootstrapping alone reports a percentage improvement as high as 14.12 %, even with a small seed set of 324 ITN entries.

Underline digital video library

Test of Time Awards

Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection

Presidential Address

A Fine-grained citation graph for biomedical academic papers: the finding-citation graph

What Makes Language Models Good-enough?

A sanity check on emergent properties

A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting | VIDEO

Evaluation after the LM boom: Frustrations, fallacies andthe future

Oral Presentation of CNS Model-Paper ID: 68 | VIDEO

CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models

tagE: Enabling an Embodied Agent to Understand Human Instructions

Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language | VIDEO

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Underline digital video library

Test of Time Awards

Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection

Presidential Address

A Fine-grained citation graph for biomedical academic papers: the finding-citation graph

What Makes Language Models Good-enough?

A sanity check on emergent properties

A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting | VIDEO

Evaluation after the LM boom: Frustrations, fallacies andthe future

Oral Presentation of CNS Model-Paper ID: 68 | VIDEO

CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models

tagE: Enabling an Embodied Agent to Understand Human Instructions​

Scaling Neural ITN for Numbers and Temporal Expressions in Tamil: Findings for an Agglutinative Low-resource Language | VIDEO

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

tagE: Enabling an Embodied Agent to Understand Human Instructions