Semanlink - Yoav Goldberg

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

@yoavgo sur Twitter : "searching by description can be very useful, but current embedding models will give you texts that are *similar* to the description, rather than texts that *adhere to it*..."

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2023-05-24 About

Reinforcement Learning for Language Models

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> I was puzzled for a while as to why we need RL for LM training, rather than just using supervised instruct tuning. I now have a convincing argument, which is also reflected in a recent talk by @johnschulman2.

1st convincing argument:

> supervised learning allows only positive feedback (we show the model a series of questions and their correct answers) while **RL allows also for negative feedback** (the model is allowed to generate an answer an get a feedback saying "this is not correct")...if you as a learner are allowed to form your own hypotheses and ask the teacher if they are correct (as in the RL setting), even an adversarial teacher can no longer trick you into latching on to a wrong hypothesis.

2nd convincing argument is about knowledge-seeking queries

> we want to encourage the model to answer based on its internal knowledge, but we don't know what this internal knowledge contains. In supervised training, we present the model with a question and its correct answer, and train the model to replicate the provided answer... But if we are succeed in training the model to generalize in [the cases it doesn't know], then we essentially teaches the model to make stuff up! it actively encourages the model to "lie".

2023-04-23 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "can you explain LangChain to me?... what does it save?"

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2023-04-19 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "there is this genre of papers that show you can train/tune only some subset of a network's weights, freezing the rest, and things still work as well as (or better than) full training/tuning..."

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2023-02-15 About

Some remarks on Large Language Models

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> There turned out to be a phase shift somewhere between 60B parameters and 175B parameters, that made language models super impressive.

> **The performance of current days language models are not obtained by language modeling**
>
>    - [Traditional] LMs are not [grounded](tag:grounded_language_learning)
> 
> **3 conceptual steps between GPT-3 and chatGPT: Instructions, code, RLHF.** The last one is, I think, the least interesting despite getting the most attention
>
> Instruction tuning: For example, the human annotators would write something like "please summarize this text", followed by some text they got, followed by a summary they produced of this text. -> Some symbols ("summarize", "translate", "formal") are used in a consistent way together with the concept/task they denote. And they always appear in the beginning of the text. -> the act of producing a summary grounded to the human concept of "summary"
>
> code: programming language code data, and specifically data that contains both natural language instructions or descriptions (in the form of code comments) and the corresponding programming language code. This produced another very direct form of grounding. the human language describes concepts (or intents), which are then realized in the form of the corresponding programs.
>
> "[RL with Human Feedback](tag:reinforcement_learning_from_human_feedback)". This is a fancy way of saying that the model now observes two humans in a conversation, one playing the role of a user, and another playing the role of "the AI", demonstrating how the AI should respond in different situations. This clearly helps the model learn how dialogs work, and how to keep track of information across dialog states (something that is very hard to learn from just "found" data).

2023-01-03 About

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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2022-09-01 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "... another step in understanding how transformer-based LMs work..."

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2022-03-30 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "our attempt at producing large-scale, sense-annotated corpora, with automatically derived word senses ..."

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2022-03-28 About

Yosi Shamay sur Twitter : "a new platform for rapid ad-hoc knowledgebase construction using extractive search...."

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2022-02-22 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "Text-based NP Enrichment"

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2021-09-28 About

(((ل()(ل() 'yoav))))👾 sur Twitter : "my two cents on why NLP as a field is focusing on the ML-ish / algorithmic / leaderboard-ish aspects (incl., now, LLMs) and not on the underlying language phenomena: it is just so much easier, on so many levels."

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2021-08-30 About

[2106.04612] Neural Extractive Search

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how to extend a
search paradigm we call “**extractive search**” with
neural similarity techniques.

> some information needs require extracting
and aggregating sub-sentence information
(words, phrases, or entities) from multiple documents
(e.g. a list of all the risk factors for a specific
disease and their number of mentions, or a comprehensive
table of startups and CEOs).

> extractive search combines
document selection with information extraction. **The query is extended with capture slots**:
these are **search terms that act as variables, whose
values should be extracted**.
> The user
is then presented with the matched documents, each
annotated with the corresponding captured spans,
as well as aggregate information over the captured
spans

Conclusion :

> We presented a system for neural extractive search.
While we found our system to be useful for scientific
search, it also has clear limitations and areas
for improvement, both in terms of accuracy (only
72.2% of the returned results are relevant, both the
alignment and similarity models generalize well to
some relations but not to others), and in terms of
scale

[Video of demo](https://www.youtube.com/watch?v=TtqWi2GgB5A&t=1832s)

2021-06-23 About

[2104.10809] Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

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2021-05-23 About

A criticism of Stochastic Parrots

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2021-01-23 About

(((ل()(ل() 'yoav)))) sur Twitter : "is there a convincingly successful application of graph convolutions in NLP you can point me to?"

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2019-12-19 About

(((ل()(ل() 'yoav)))) sur Twitter : what do you think should be an interesting and important achievement of 2020 for NLP?