Canada

This work presents results using multiple large pretrained language models showing that models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times, replicating and expanding upon earlier results limited to just GPT-2 (Oh et al., 2022). First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, providing strong empirical support for this trend. Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to &#39;memorize&#39; sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pretrained language models to study human language processing.

&lt;iframe src=&quot;https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/a40e47ea-2788-434b-a797-7746bee16cb9&quot; width=&quot;300&quot; height=&quot;400&quot;&gt;&lt;/iframe&gt;

ACL 2023

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

surprisal theory

cognitive modeling

large language models

This work presents results using multiple large pretrained language models showing that models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times, replicating and expanding upon earlier results limited to just GPT-2 (Oh et al., 2022). First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, providing strong empirical support for this trend. Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to 'memorize' sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pretrained language models to study human language processing.

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/a40e47ea-2788-434b-a797-7746bee16cb9" width="300" height="400"></iframe>

technical paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
<br>The conference will be held in Toronto, Canada, July 9-14, 2023.<br><br>
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience.<br>
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**
<br><br><br><br>

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/c2bbd779-6378-4ed1-a6b1-84822c9dbe30" width="300" height="400"></iframe>

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs. To the best of our knowledge, we are not aware of any LLMs that indicate their level of confidence in their responses which fuels a trust deficit in these models impeding their adoption. To address this deficiency, we propose 'MathPrompter', a technique that improves performance of LLMs on arithmetic problems along with increased reliance in the predictions. MathPrompter uses the Zero-shot chain-of-thought prompting technique to generate multiple algebraic expressions or python functions to solve the same math problem in different ways and thereby raise the confidence level in the output results. This is in contrast to other prompt based CoT methods, where there is no check on the validity of the intermediate steps followed. Our technique improves over state-of-the-art on the 'MultiArith' dataset (78.7% -> 92.5%) evaluated using 175B parameter GPT-based LLM.

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/08a4a6f7-8143-4237-8930-b597a284727b" width="300" height="400"></iframe>

MathPrompter: Mathematical Reasoning using Large Language Models

"Large language models trained on code have shown great potential to increase productivity of software developers. Several execution-based benchmarks have been proposed to evaluate functional correctness of model-generated code on simple programming problems. Nevertheless, it is expensive to perform the same evaluation on complex real-world projects considering the execution cost. On the other hand, static analysis tools such as linters, which can detect errors without running the program, haven't been well explored for evaluating code generation models. In this work, we propose a static evaluation framework to quantify static errors in Python code completions, by leveraging Abstract Syntax Trees. Compared with execution-based evaluation, our method is not only more efficient, but also applicable to code in the wild. For experiments, we collect code context from open source repos to generate one million function bodies using public models. Our static analysis reveals that Undefined Name and Unused Variable are the most common errors among others made by language models.
Through extensive studies, we also show the impact of sampling temperature, model size, and context on static errors in code completions."

A Static Evaluation of Code Completion by Large Language Models

We present an efficient and reliable approach to Natural Language Querying (NLQ) on databases (DB) which is not based on text-to-SQL type semantic parsing. Our approach simplifies the NLQ on structured data problem to the following "bread and butter" NLP tasks: (a) Domain classification, for choosing which DB table to query, whether the question is out-of-scope (b) Multi-head slot/entity extraction (SE) to extract the field criteria and other attributes such as its role (filter, sort etc) from the raw text and (c) Slot value disambiguation (SVD) to resolve/normalize raw spans from SE to format suitable to query a DB. This is a general purpose, DB language agnostic approach and the output can be used to query any DB and return results to the user. Also each of these tasks is extremely well studied, mature, easier to collect data for and enables better error analysis by tracing problems to specific components when something goes wrong.

An efficient method for Natural Language Querying on Structured Data

Large language models (LLMs) not only learn natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs. To this end, we present KosBi, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperClova (30B and 82B), and GPT-3.

KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications

"We train and deploy language models (LMs) with federated learning (FL) and differential privacy (DP) in Google Keyboard (Gboard). The recent DP-Follow the Regularized Leader (DP-FTRL) algorithm is applied to achieve meaningfully formal DP guarantees without requiring uniform sampling of clients. 
To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation of training. 
With the help of pretraining on public data, we trained and deployed more than fifteen Gboard LMs that achieve high utility and $\rho-$zCDP privacy guarantees with $\rho \in (0.3, 2)$, with one model additionally trained with secure aggregation.
We summarize our experience and provide concrete suggestions on DP training for practitioners."

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/426ed18c-7356-4bee-a8a2-62af6a74450c" width="300" height="400"></iframe>

Federated Learning of Gboard Language Models with Differential Privacy

Conversational NLU providers often need to scale to thousands of intent-classification models where new customers often face the cold-start problem. Scaling to so many customers puts a constraint on storage space as well. In this paper, we explore four different zero and few-shot intent classification approaches with this low-resource constraint: 1) domain adaptation, 2) data augmentation, 3) zero-shot intent classification using descriptions large language models (LLMs), and 4) parameter-efficient fine-tuning of instruction-finetuned language models. Our results show that all these approaches are effective to different degrees in low-resource settings. Parameter-efficient fine-tuning using T-few recipe on Flan-T5 yields the best performance even with just one sample per intent. We also show that the zero-shot method of prompting LLMs using intent descriptions is also very competitive.

<iframe src="https://app.sli.do/event/uyG7EHDP5trs2LHubv2Gj8/embed/polls/a97e93b1-c289-49a4-a774-cb952e673ef9" width="300" height="400"></iframe>

Exploring Zero and Few-shot Techniques for Intent Classification

The ever-increasing size of language models curtails their widespread access to the community, thereby galvanizing many companies and startups into offering access to large language models through APIs. One particular API, suitable for dense retrieval, is the semantic embedding API that builds vector representations of a given text. With a growing number of APIs at our disposal, in this paper, our goal is to analyze semantic embedding APIs in realistic retrieval scenarios in order to assist practitioners and researchers in finding suitable services according to their needs. Specifically, we wish to investigate the capabilities of existing APIs on domain generalization and multilingual retrieval. For this purpose, we evaluate the embedding APIs on two standard benchmarks, BEIR, and MIRACL. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective on English, in contrast to the standard practice, i.e., employing them as first-stage retrievers. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best albeit at a higher cost. We hope our work lays the groundwork for thoroughly evaluating APIs that are critical in search and more broadly, in information retrieval.

Evaluating Embedding APIs for Information Retrieval

Query rewriting (QR) is an important technique for user friction (i.e. recovering ASR error or system error) reduction and contextual carryover (i.e. ellipsis and co-reference) in conversational AI systems. Recently, generation-based QR models have achieved promising results on these two tasks separately. Although these two tasks have many similarities such as they both use the previous dialogue along with the current request as model input, there is no unified model to solve them jointly. To this end, we propose a unified contextual query rewriting model that unifies QR for both reducing friction and contextual carryover purpose. Moreover, we involve multiple auxiliary tasks such as trigger prediction and NLU interpretation tasks to boost the performance of the rewrite. We leverage the text-to-text unified framework which uses independent tasks with weighted loss to account for task importance. Then we propose new unified multitask learning strategies including a sequential model which outputs one sentence for multi-tasks, and a hybrid model where some tasks are independent and some tasks are sequentially generated. Our experimental results demonstrate the effectiveness of the proposed unified learning methods.

Unified Contextual Query Rewriting

Identifying granular and actionable topics from customer questions (CQ) posted on e-commerce websites helps surface the missing information expected by customers on the product detail page (DP), provide insights to brands and sellers on what critical product information that the customers are looking before making a purchase decision and helps enrich the catalog quality to improve the overall customer experience (CX). We propose a weakly supervised Hierarchical Multi-task Classification Framework (HMCF) to identify topics from customer questions at various granularities. Complexity lies in creating a list of granular topics (taxonomy) for 1000s of product categories and building a scalable classification system. To this end, we introduce a clustering based Taxonomy Creation and Data Labeling (TCDL) module for creating taxonomy and labelled data with minimal supervision. Using TCDL module, taxonomy and labelled data creation task reduces to 2 hours as compared to 2 weeks of manual efforts by a subject matter expert. For classification, we propose a two level HMCF that performs multi-class classification to identify coarse level-1 topic and leverages NLI based label-aware approach to identify granular level-2 topic. We showcase that HMCF (based on BERT and NLI) a) achieves absolute improvement of 13% in Top-1 accuracy over single-task non-hierarchical baselines b) learns a generic domain invariant function that can adapt to constantly evolving taxonomy (open label set) without need of re-training. c) reduces model deployment efforts significantly since it needs only one model that caters to 1000s of product categories.

Premium content

Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?

Downloads

Next from ACL 2023

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES