arXiv subjects

Karmanya Aggarwal

Publications and source records attributed to Karmanya Aggarwal.

4 recordsLinked to original sources

arXiv2023

FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information

Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large language models (LLMs) that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. However, previous work used game state information that was heuristically created and was not a true gold standard game state. We present FIREBALL, a large dataset containing nearly 25,000 unique sessions from real D&D gameplay on Discord with true game state info. We recorded game play sessions of players who used the Avrae bot, which was developed to aid people in playing D&D online, capturing language, game commands and underlying game state information. We demonstrate that FIREBALL can improve natural language generation (NLG) by using Avrae state information, improving both automated metrics and human judgments of quality. Additionally, we show that LLMs can generate executable Avrae commands, particularly after finetuning.

cs.CL↗

arXiv2021

Does Putting a Linguist in the Loop Improve NLU Data Collection?

Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples during crowdsourcing should make mitigation more efficient, especially when done iteratively. We take natural language inference as a test case and ask whether it is beneficial to put a linguist `in the loop' during data collection to dynamically identify and address gaps in the data by introducing novel constraints on the task. We directly compare three data collection protocols: (i) a baseline protocol, (ii) a linguist-in-the-loop intervention with iteratively-updated constraints on the task, and (iii) an extension of linguist-in-the-loop that provides direct interaction between linguists and crowdworkers via a chatroom. The datasets collected with linguist involvement are more reliably challenging than baseline, without loss of quality. But we see no evidence that using this data in training leads to better out-of-domain model performance, and the addition of a chat platform has no measurable effect on the resulting dataset. We suggest integrating expert analysis \textit{during} data collection so that the expert can dynamically address gaps and biases in the dataset.

cs.CL↗

arXiv2021

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate.

cs.CL↗

arXiv2020

Trawling for Trolling: A Dataset

The ability to accurately detect and filter offensive content automatically is important to ensure a rich and diverse digital discourse. Trolling is a type of hurtful or offensive content that is prevalent in social media, but is underrepresented in datasets for offensive content detection. In this work, we present a dataset that models trolling as a subcategory of offensive content. The dataset was created by collecting samples from well-known datasets and reannotating them along precise definitions of different categories of offensive content. The dataset has 12,490 samples, split across 5 classes; Normal, Profanity, Trolling, Derogatory and Hate Speech. It encompasses content from Twitter, Reddit and Wikipedia Talk Pages. Models trained on our dataset show appreciable performance without any significant hyperparameter tuning and can potentially learn meaningful linguistic information effectively. We find that these models are sensitive to data ablation which suggests that the dataset is largely devoid of spurious statistical artefacts that could otherwise distract and confuse classification models.

cs.CY↗