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> Second, text ought to be tokenized (a term used in natural language processing wherein text is assigned a numerical value) at many different levels (character

> Second, text ought to be tokenized (a term used in natural language processing wherein text is assigned a numerical value) at many different levels (character, word, sentence, paragraph, section, etc.) in order to make predictions that are relevant to the excerpt of a text, while also remaining consistent to the broader document. This is challenging because doing so involves a tremendous amount of computational resources. However, it may be necessary to accurately capture meaning at different levels of abstraction. I don't think this would be an improvement per se. The way Byte Pair Encodings are constructed that GPT-3 (and GPT-2) use is that they already take higher-level text representations and compress it down (into tokens), which are then reflected in the training of the model.

Tarq0n 6y ago
And the attention mechanism should take care of "sentence, paragraph, section" level context. Attributing this to tokenization is a weird mistake to make.
- gavelin 6y ago
  Tarq0n: Also a great point. The question then is whether the attention mechanism is being triggered on the proper word or character sequences. Lawyers employ a form of attention mechanism when “issue spotting.” For example, in a non-compete I might scan for the duration (how many years until the client can join a similar venture?) and breadth (what is the definition of a similar venture?). For an attention mechanism to work well for legal summaries, it seems to me that it must trigger on many relevant context triggers at different levels. As an extreme example, if I put 50 contracts involving multiple different parties into one big document and the attention mechanism was triggered on the first document title, would the subsequent document titles sufficiently demarcate a new contract to reset the context? Or would the attention stay “on” the first high accuracy context match and mix up the terms and parties? In context of the paper, GPT-3 missed a ton of issues, indicating to me that the attention mechanism is not being properly triggered. Again I may be wrong that tokenizing and predicting based off of clauses, paragraphs and sections would help improve the output, but that was one way I thought would capture the different levels of context.
gavelin 6y ago
minimaxir: Great insight and probably merits an edit for precision. My understanding is that Byte-Pair encoding is done at the character and word level (and maybe even the sub-character level), but not at the higher-level representations such as paragraph, section—and beyond. Am I mistaken? Taking a few steps back, is that an effective way to pinpoint context? The goal should be to properly ascertain context at multiple levels. When I am reviewing a document, I scan the document title and section headings to grasp the structure of the document prior to diving into the relevant clauses and their elements. If there are external references, I will integrate them before reading the clause in order to capture the complete rule. A crucial mistake in contractual interpretation is falsely attributing an element from one rule or section to another, or excluding an element. What applies in A context might not apply in B context, or it may be conditional on another factor C. The criticism I intended to make was that GPT-3 likely is not (accurately) identifying the right context “bucket” before making the prediction and that perhaps could be improved by tokenizing different levels of context. I may have falsely reasoned that this could be best accomplished through tokenization at different levels. In context of the paper, GPT-3 referenced Tinder and MommyMeet when I inputted Linkedin’s Privacy Policy. Also, if GPT-3 contains Section 230 in its training data, it did not look to the definition list at the bottom of the statute to the define a key term (I excluded the definitions from the input). My hunch is that a better approach would localize based on the document, section and clause type to precisely narrow the context before utilizing character and word level predictions.
- joe_the_user 6y ago
  I would claim it is easy to think you're seeing GPT-3 fail because it's taking the wrong association path (not noticing the hierarchical decomposition of the context). But the general problem is that there is no fixed decomposition of the meaning of a text. It's tempting to think a "simple" procedure like summary doesn't need "deep" meaning but it doesn't seem like that's the case. It should especially be noted a lot of the plausible "summaries" could be summaries of any privacy policy or just what people say about privacy policies on the net. It would have been interesting to give the system a novel bit of text to interpret instead.
  - 6y ago

Tarq0n 6y ago

And the attention mechanism should take care of "sentence, paragraph, section" level context. Attributing this to tokenization is a weird mistake to make.

gavelin 6y ago
Tarq0n: Also a great point. The question then is whether the attention mechanism is being triggered on the proper word or character sequences. Lawyers employ a form of attention mechanism when “issue spotting.” For example, in a non-compete I might scan for the duration (how many years until the client can join a similar venture?) and breadth (what is the definition of a similar venture?). For an attention mechanism to work well for legal summaries, it seems to me that it must trigger on many relevant context triggers at different levels. As an extreme example, if I put 50 contracts involving multiple different parties into one big document and the attention mechanism was triggered on the first document title, would the subsequent document titles sufficiently demarcate a new contract to reset the context? Or would the attention stay “on” the first high accuracy context match and mix up the terms and parties? In context of the paper, GPT-3 missed a ton of issues, indicating to me that the attention mechanism is not being properly triggered. Again I may be wrong that tokenizing and predicting based off of clauses, paragraphs and sections would help improve the output, but that was one way I thought would capture the different levels of context.

gavelin 6y ago

minimaxir: Great insight and probably merits an edit for precision. My understanding is that Byte-Pair encoding is done at the character and word level (and maybe even the sub-character level), but not at the higher-level representations such as paragraph, section—and beyond. Am I mistaken? Taking a few steps back, is that an effective way to pinpoint context? The goal should be to properly ascertain context at multiple levels. When I am reviewing a document, I scan the document title and section headings to grasp the structure of the document prior to diving into the relevant clauses and their elements. If there are external references, I will integrate them before reading the clause in order to capture the complete rule. A crucial mistake in contractual interpretation is falsely attributing an element from one rule or section to another, or excluding an element. What applies in A context might not apply in B context, or it may be conditional on another factor C. The criticism I intended to make was that GPT-3 likely is not (accurately) identifying the right context “bucket” before making the prediction and that perhaps could be improved by tokenizing different levels of context. I may have falsely reasoned that this could be best accomplished through tokenization at different levels. In context of the paper, GPT-3 referenced Tinder and MommyMeet when I inputted Linkedin’s Privacy Policy. Also, if GPT-3 contains Section 230 in its training data, it did not look to the definition list at the bottom of the statute to the define a key term (I excluded the definitions from the input). My hunch is that a better approach would localize based on the document, section and clause type to precisely narrow the context before utilizing character and word level predictions.

joe_the_user 6y ago
I would claim it is easy to think you're seeing GPT-3 fail because it's taking the wrong association path (not noticing the hierarchical decomposition of the context). But the general problem is that there is no fixed decomposition of the meaning of a text. It's tempting to think a "simple" procedure like summary doesn't need "deep" meaning but it doesn't seem like that's the case. It should especially be noted a lot of the plausible "summaries" could be summaries of any privacy policy or just what people say about privacy policies on the net. It would have been interesting to give the system a novel bit of text to interpret instead.
- 6y ago