17 research outputs found
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Towards Robust Long-form Text Generation Systems
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to world knowledge and the input prompt; (2) it is difficult to accurately evaluate the quality of long-form generated text; (3) it is difficult to identify whether a piece of long-form text was AI-generated, a task necessary to prevent widespread misinformation and plagiarism.
In this thesis I design algorithms aimed at making progress towards these three issues in current LLMs. I will first describe a retrieval-augmented system we built for long-form question answering, to improve factual correctness of long-form generated text. However, a careful empirical analysis reveals issues related to input/output consistency of generated text, and an inherent difficulty in evaluation. I will then describe our model RankGen, which uses large-scale contrastive learning on documents to significantly outperform competing long-form text generation methods to generate text more faithful to the input. Next, I will describe our efforts to improve human evaluation of long-form generation (issue #2) by proposing the LongEval guidelines. LongEval is a set of three simple empirically-motivated ideas to make human evaluation of long-form generation more consistent, less expensive, and cognitively easier for evaluators. Finally, I describe my work on AI-generated text detection (issue #3), and showcase the brittleness of existing methods to paraphrasing attacks I designed. I will describe a simple new AI-generated text detection algorithm using information retrieval, which is significantly more robust to paraphrasing attacks.
Finally, I conclude this thesis with some future research directions that I am excited about, including plan-based long-form text generation, and a deeper dive into understanding large language model training dynamics
Revisiting the Importance of Encoding Logic Rules in Sentiment Classification
We analyze the performance of different sentiment classification models on
syntactically complex inputs like A-but-B sentences. The first contribution of
this analysis addresses reproducible research: to meaningfully compare
different models, their accuracies must be averaged over far more random seeds
than what has traditionally been reported. With proper averaging in place, we
notice that the distillation model described in arXiv:1603.06318v4 [cs.LG],
which incorporates explicit logic rules for sentiment classification, is
ineffective. In contrast, using contextualized ELMo embeddings
(arXiv:1802.05365v2 [cs.CL]) instead of logic rules yields significantly better
performance. Additionally, we provide analysis and visualizations that
demonstrate ELMo's ability to implicitly learn logic rules. Finally, a
crowdsourced analysis reveals how ELMo outperforms baseline models even on
sentences with ambiguous sentiment labels.Comment: EMNLP 2018 Camera Read
A Study of All-Convolutional Encoders for Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a popular sequence prediction
approach for automatic speech recognition that is typically used with models
based on recurrent neural networks (RNNs). We explore whether deep
convolutional neural networks (CNNs) can be used effectively instead of RNNs as
the "encoder" in CTC. CNNs lack an explicit representation of the entire
sequence, but have the advantage that they are much faster to train. We present
an exploration of CNNs as encoders for CTC models, in the context of
character-based (lexicon-free) automatic speech recognition. In particular, we
explore a range of one-dimensional convolutional layers, which are particularly
efficient. We compare the performance of our CNN-based models against typical
RNNbased models in terms of training time, decoding time, model size and word
error rate (WER) on the Switchboard Eval2000 corpus. We find that our CNN-based
models are close in performance to LSTMs, while not matching them, and are much
faster to train and decode.Comment: Accepted to ICASSP-201
Generating Question-Answer Hierarchies
The process of knowledge acquisition can be viewed as a question-answer game
between a student and a teacher in which the student typically starts by asking
broad, open-ended questions before drilling down into specifics (Hintikka,
1981; Hakkarainen and Sintonen, 2002). This pedagogical perspective motivates a
new way of representing documents. In this paper, we present SQUASH
(Specificity-controlled Question-Answer Hierarchies), a novel and challenging
text generation task that converts an input document into a hierarchy of
question-answer pairs. Users can click on high-level questions (e.g., "Why did
Frodo leave the Fellowship?") to reveal related but more specific questions
(e.g., "Who did Frodo leave with?"). Using a question taxonomy loosely based on
Lehnert (1978), we classify questions in existing reading comprehension
datasets as either "general" or "specific". We then use these labels as input
to a pipelined system centered around a conditional neural language model. We
extensively evaluate the quality of the generated QA hierarchies through
crowdsourced experiments and report strong empirical results.Comment: ACL camera ready + technical note on pipeline modifications for demo
(15 pages
Syntactically Supervised Transformers for Faster Neural Machine Translation
Standard decoders for neural machine translation autoregressively generate a
single target token per time step, which slows inference especially for long
outputs. While architectural advances such as the Transformer fully parallelize
the decoder computations at training time, inference still proceeds
sequentially. Recent developments in non- and semi- autoregressive decoding
produce multiple tokens per time step independently of the others, which
improves inference speed but deteriorates translation quality. In this work, we
propose the syntactically supervised Transformer (SynST), which first
autoregressively predicts a chunked parse tree before generating all of the
target tokens in one shot conditioned on the predicted parse. A series of
controlled experiments demonstrates that SynST decodes sentences ~ 5x faster
than the baseline autoregressive Transformer while achieving higher BLEU scores
than most competing methods on En-De and En-Fr datasets.Comment: 9 pages, 5 figures, accepted to ACL 201
On the Risks of Stealing the Decoding Algorithms of Language Models
A key component of generating text from modern language models (LM) is the
selection and tuning of decoding algorithms. These algorithms determine how to
generate text from the internal probability distribution generated by the LM.
The process of choosing a decoding algorithm and tuning its hyperparameters
takes significant time, manual effort, and computation, and it also requires
extensive human evaluation. Therefore, the identity and hyperparameters of such
decoding algorithms are considered to be extremely valuable to their owners. In
this work, we show, for the first time, that an adversary with typical API
access to an LM can steal the type and hyperparameters of its decoding
algorithms at very low monetary costs. Our attack is effective against popular
LMs used in text generation APIs, including GPT-2 and GPT-3. We demonstrate the
feasibility of stealing such information with only a few dollars, e.g.,
, , , and for the four versions of GPT-3
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
The rise in malicious usage of large language models, such as fake content
creation and academic plagiarism, has motivated the development of approaches
that identify AI-generated text, including those based on watermarking or
outlier detection. However, the robustness of these detection algorithms to
paraphrases of AI-generated text remains unclear. To stress test these
detectors, we build a 11B parameter paraphrase generation model (DIPPER) that
can paraphrase paragraphs, condition on surrounding context, and control
lexical diversity and content reordering. Using DIPPER to paraphrase text
generated by three large language models (including GPT3.5-davinci-003)
successfully evades several detectors, including watermarking, GPTZero,
DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection
accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of
1%), without appreciably modifying the input semantics.
To increase the robustness of AI-generated text detection to paraphrase
attacks, we introduce a simple defense that relies on retrieving
semantically-similar generations and must be maintained by a language model API
provider. Given a candidate text, our algorithm searches a database of
sequences previously generated by the API, looking for sequences that match the
candidate text within a certain threshold. We empirically verify our defense
using a database of 15M generations from a fine-tuned T5-XXL model and find
that it can detect 80% to 97% of paraphrased generations across different
settings while only classifying 1% of human-written sequences as AI-generated.
We open-source our models, code and data.Comment: NeurIPS 2023 camera ready (32 pages). Code, models, data available in
https://github.com/martiansideofthemoon/ai-detection-paraphrase